Statistical Modeling

Statistical Modeling Engineer Jobs

11 jobs found

Today

Staff Data Engineer - Experimentation Platform

Intuit San Diego, CA

San Diego, CA

Today

$189k - $255k/yr Full time Backend Developer Analytics Analytics Engineer

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Job Description

Intuit is seeking a Staff Data Engineer to support the Experimentation Platform team, also known as IXP. The role is centered on enabling experimentation-driven, data-driven decisions by partnering across Engineering, ML, Search, Data Science, and Data Engineering to design experiments, compute metrics, and apply analytics and causal approaches at scale.

Within IXP, the team builds an enterprise capability that helps Intuit move from hypothesis to action with speed and reliability. Teams across TurboTax, Mint, and QuickBooks use IXP, giving individuals a way to test ideas with measurable results.

What you will do

Work closely with an interdisciplinary group of Engineering, ML, Search, Data Science, Data Engineering, and Analytics practitioners to develop capabilities that improve product decision-making and creation
Drive innovation by identifying and applying analytics, causal inference, experimentation, and machine learning techniques
Apply analytics and statistical methods to solve business problems, including hypothesis testing and causal inference foundations
Lead the design, analysis, and interpretation of experiments
Perform proactive data exploration on engagement behaviors to uncover future opportunities
Identify, compute, and validate metrics used to measure success for a product area
Build strong partnerships with stakeholders across product, engineering, operations, design, and consumer research
Share research and insights with audiences across all levels of the company

Minimum qualifications

BS or MS in Statistics, Mathematics, Engineering, or a related quantitative field
8+ years of coding experience in Java, Javascript, Go, or equivalent
8+ years designing and operating high-throughput, low-latency distributed systems processing data at petabyte scale
Prior experience focused on experiment design and/or A/B testing at scale
Ability to apply the statistical underpinnings of experimentation, including hypothesis testing and p-values, and causal inference
Familiarity with statistical analysis and programming languages such as R or Python
Proven experience as a Data Engineer, including data warehouse design and data modeling
Expertise in SQL and experience with big data technologies such as Hadoop, Spark, Kafka, or similar
Experience with data visualization tools such as Tableau or Power BI, or similar platforms
Experience building reusable frameworks, SDKs, or platform libraries used by other engineering teams, with a focus on developer experience
Strong programming skills in Python, Java, SQL, and Scala
Experience with cloud platforms including AWS, Azure, or Google Cloud
Familiarity with machine learning algorithms and applying them in data analysis
Ability to work independently while partnering cross-functionally to manage end-to-end product development
Ability to communicate technical and statistical concepts clearly and concisely across varied audiences

Technology

Java, Javascript, Go, R, Python, SQL, Hadoop, Spark, Kafka, Tableau, Power BI, Scala, AWS, Azure, Google Cloud

Compensation

Intuit provides a competitive compensation package with a strong pay for performance rewards approach. This position may be eligible for a cash bonus, equity rewards, and benefits based on applicable plans and programs, with pay determined by job-related knowledge, skills, experience, and work location.

The expected base pay range for this position is:

San Diego, CA: USD 188,500 - 255,000 per year
Mountain View: USD 202,500 - 274,000 per year

Intuit also conducts regular comparisons across categories of ethnicity and gender to drive ongoing fair pay.

Location: San Diego, CA 92129 (onsite)

Salary: USD 188,500 - 255,000 per year

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9d ago

Staff AI Engineer

General Motors Warren, MI

Warren, MI

9d ago

Full time Agentic Ai Ai Agent Ai Agent Platform

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Job Description

General Motors is seeking a Staff AI Engineer to design and deliver trustworthy AI systems and data products end to end. This onsite role in Warren, MI focuses on turning ambiguous business needs into measurable AI objectives, building production-ready capabilities, and operating them reliably across Sales, Service, Marketing, and Global Markets.

What you will do

Translate unclear business problems into defined AI objectives, solution strategies, evaluation plans, and measurable success criteria.
Develop, validate, and improve predictive and forecasting solutions, including classification, segmentation, optimization, natural-language, generative-AI, and decision-support approaches.
Select practical statistical, machine-learning, and AI methods aligned to the decision, available data, operational constraints, risk, and expected value.
Apply modern AI patterns when appropriate, including experimentation, time-series forecasting, causal analysis, retrieval-augmented generation, and agentic workflows.
Partner with stakeholders to determine when AI is the right approach versus simpler analytics, rules, or workflow automation.
Define data requirements and work with data engineering, platform, and business teams to establish reliable, documented data sources.
Build scalable data capabilities across ingestion, transformation, aggregation, features, embeddings, and knowledge pipelines using batch or streaming patterns.
Create reusable data products, analytical datasets, semantic models, feature assets, and retrieval indexes for multiple use cases.
Conduct exploratory analysis and data-quality work such as schema validation, feature selection, leakage detection, and data-availability analysis across structured and unstructured sources.
Work with customer, vehicle, dealer, sales, service, warranty, incentive, document, conversation, and operational datasets.
Deliver production-grade AI applications, services, APIs, assistants, dashboards, and workflow tools so AI capabilities are usable by end users.
Design interfaces connecting data pipelines, models, foundation models, tools, backend services, and front-end or workflow applications.
Implement retrieval, grounding, tool use, orchestration, prompt management, evaluation, and human-in-the-loop patterns where they fit.
Develop secure, testable, observable, and maintainable software with code review, version control, and automated testing.
Collaborate with architects and software engineers on structure, integration patterns, authentication, authorization, deployment, and operational support.
Establish evaluation frameworks tied to real-world business outcomes, then assess quality factors such as calibration, bias, interpretability, robustness, factuality, groundedness, safety, usability, latency, and operational fit.
Design and analyze experiments, pilots, red-team exercises, and champion/challenger validation to manage risk before broad adoption.
Deploy as APIs, batch processes, scheduled workflows, applications, assistants, or decision-support capabilities, with strong practices for versioning, lineage, prompt and model tracking, release management, rollback, and reproducibility.
Implement monitoring for data quality, drift, model and response quality, safety signals, application health, latency, availability, usage, and business outcomes.
Troubleshoot production issues, identify root causes, and improve models, pipelines, services, prompts, and user experiences through iterative refinement.
Apply responsible-AI, privacy, security, and data-governance principles, including controls for access, auditability, explainability, and human oversight.
Document assumptions, limitations, intended use, prohibited use, evaluation results, and operational ownership; coordinate with security, privacy, architecture, legal, and governance teams for compliant enterprise deployment.
Act as a technical authority across applied AI, machine learning, data engineering, application development, and AI platform patterns, raising quality through reusable patterns, reference implementations, documentation, testing, observability, and reproducibility.
Coach data scientists, analysts, engineers, and citizen builders on modeling, data, software development, and responsible-AI practices.
Support teams evaluating enterprise tooling such as Databricks, Azure AI, Azure AI Foundry, Glean, and other platforms when they improve delivery without sacrificing quality or governance.
Share lessons learned, reusable components, reference architectures, evaluation methods, and practical guidance across the AI Center and partner organizations.

What you bring

Bachelor’s degree in Computer Science, Data Science, Statistics, Mathematics, Engineering, or a related field (advanced degree preferred).
8+ years of professional experience across AI, machine learning, data science, data engineering, software engineering, application development, or a related discipline.
Proven experience moving AI, machine-learning, analytics, or data products from problem definition and proof of concept through production deployment and ongoing operations.
Strong Python and SQL skills, including production-quality code, testing, version control, documentation, and debugging.
Hands-on experience with tools such as Pandas, NumPy, scikit-learn, PyTorch, TensorFlow, or equivalent libraries.
Experience building or integrating AI applications, APIs, services, data pipelines, dashboards, and workflow tools.
Experience with model development, feature engineering, experiment design, statistical analysis, AI evaluation, and communicating results to nontechnical audiences.
Experience deploying and operating solutions via APIs, batch pipelines, notebooks-to-production workflows, applications, assistants, or similar production patterns.
Practical understanding of software and data engineering practices including CI/CD, automated testing, observability, dependency management, data quality, and release management.
Experience with large-scale data platforms such as Databricks, Spark/PySpark, cloud data warehouses, or equivalent technologies.
Ability to work independently, make sound technical tradeoffs, and deliver in a fast-changing cross-functional environment.

Technologies

Python, SQL, Pandas, NumPy, scikit-learn, PyTorch, TensorFlow
Databricks, Spark, PySpark
CI/CD, Azure AI, Azure AI Foundry, Glean
REST APIs, MLflow

Preferred qualifications

Master’s or PhD in Computer Science, Statistics, Machine Learning, Operations Research, Mathematics, Engineering, or a related quantitative field.
Experience in automotive, sales, service, marketing, customer analytics, dealer analytics, warranty, incentives, forecasting, or other operationally complex domains.
Experience building internal AI platforms, developer tools, data applications, decision-support products, assistants, or enterprise workflow solutions.
Experience with large language models including prompt engineering, retrieval-augmented generation, embeddings, vector search, tool calling, agentic workflows, or AI application evaluation.
Experience with causal inference, time-series forecasting, optimization, recommendation systems, natural-language processing, or generative-AI-enabled workflows.
Experience with MLflow or comparable tools for experiment tracking, model registry, evaluation, and lifecycle management.
Experience with Azure, Azure AI Foundry, Databricks, REST APIs, containerized deployment, CI/CD, cloud-native data services, and application observability.
Experience defining model governance, responsible-AI controls, data governance, security controls, interpretability practices, or risk-based evaluation standards.
Experience quantifying financial impact and partnering with Finance or business leaders to validate value realization.
Familiarity with enterprise AI and developer platforms such as Glean, Azure AI Foundry, Databricks, GitHub, or comparable platforms.

Immigration / sponsorship: GM does not provide immigration-related sponsorship for this role. Do not apply if you will need GM immigration sponsorship now or in the future.

Remote work / reporting requirement: This role is based remotely; however, if the selected candidate lives within a specific mile radius of a GM hub, they are expected to report to the location three times a week (or at another frequency directed by their manager).

Relocation: This job is not eligible for relocation benefits. Any relocation costs would be the responsibility of the selected candidate.

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10d ago

Sr Machine Learning Engineer- Delivery Marketplace

Uber Sunnyvale, CA

Sunnyvale, CA

10d ago

$202k - $224k/yr Full time Backend Developer Ai Ml Artificial Intelligence

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Job Description

Build machine learning systems that help improve dispatch decisions, ETAs, pickup optimization, and trip reliability across Uber’s Delivery Marketplace. This onsite role in Sunnyvale, CA focuses on designing, developing, and bringing advanced ML models and matching algorithms into production at massive scale, with strong ownership of technical strategy and roadmap execution.

What you’ll do

Own technical strategy and execution across multiple quarter horizons, including platform-level architecture decisions, executive communication and alignment, technical mentorship, and technical influence across teams.
Develop novel marketplace products that combine prediction and forecasting with optimization to improve both business efficiency and user experience.
Lead end-to-end ML solution delivery for large-scale distributed systems serving billions of trips, from design through productionization.
Build transformer-based models using pre-training and fine-tuning to generate embeddings and natural language profiles that support Marketplace use cases.

What you’ll bring

Ph.D., M.S., or Bachelor in Computer Science or Mathematics with a focus on Machine Learning (or equivalent technical background) with exceptional demonstrated impact.
3+ years leading development and deployment of ML models in large-scale production environments at top-tier ML organizations (for example, 1M+ predictions/sec or 100M+ users), with a track record of delivering business impact over multiple quarters.
Programming proficiency in Python, Scala, Java, or Go.
Large-scale data systems and real-time processing experience (such as Spark, Ray, Flink) and familiarity with microservices architectures.
End-to-end ML production skills, including training, productionization, and monitoring across offline pipelines, online serving, and MLOps.
Strong foundation in modern ML algorithms.

Tools and technologies

Python, Scala, Java, Go, Spark, Ray, Flink, microservices architectures, machine learning, deep learning, causal modeling, reinforcement learning, transformer-based models, embeddings, natural language profiles, MLOps

Benefits

Eligible to participate in Uber’s bonus program.
May be offered an equity award and other types of compensation.
All full-time employees are eligible to participate in a 401(k) plan.
Eligible for various benefits.

Salary

USD 202,000 - 224,000 per year (base salary range).

Location and in-office expectations

Offices remain key to collaboration and Uber’s culture. Unless approved for full remote work, employees must spend at least 50% of their time in-office. Some roles may require full-time in-office presence.

Preferred qualifications

Experience developing and deploying ML models for large-scale business problems.
Track record translating complex business problems into technical solutions and driving cross-functional projects.
Excellent communication skills to lead initiatives across multiple product areas.
Proficiency in deep learning, causal machine learning, or reinforcement learning.
Proficiency with large embedding and language models.

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25d ago

Machine Learning Engineer 2, Business Platform

Intuit Mountain View, CA

Mountain View, CA

25d ago

$141k - $190k/yr Full time Python A/B Testing Advanced Analytics

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Job Description

Intuit seeks a Machine Learning Engineer 2 to join a data science team responsible for conceiving, coding, and deploying data science models at scale. The role emphasizes data preparation, model development and deployment, experimentation through A/B testing and statistical analysis, and the building of agentic systems.

Responsibilities

Identify data sources, obtain access, import datasets, clean and prepare data, and enable datasets to be “machine learning ready”.
Collaborate with data scientists to create and refine features from underlying data and to build pipelines used for training and deployment.
Partner with data scientists to understand, implement, refine, and design machine learning and other algorithms.
Run recurring A/B tests, collect results, conduct statistical analysis, and draw conclusions regarding model impact.
Work cross-functionally with product managers, data scientists, and product engineers, communicating findings to peers and leaders.
Evaluate emerging technology shifts to assess how they may support customer benefits.

Requirements

BS, MS, or PhD degree in Computer Science or a related field (or equivalent work experience).
1+ years of experience.
Working knowledge of data science tools and frameworks, including Python, Scikit, NLTK, Numpy, Pandas, TensorFlow, Keras, R, and Spark.
Basic understanding of machine learning techniques such as classification, regression, and clustering.
Knowledge of machine learning principles including training and validation.
Familiarity with data query and data processing tools, including SQL.
Computer science fundamentals including data structures, algorithms, performance complexity, and how computer architecture influences software performance such as I/O and memory tuning.
Software engineering fundamentals, including version control systems (Git, Github), workflows, and the ability to write production-ready code.
Experience deploying highly scalable software supporting millions or more users.
Experience with GPU acceleration, including CUDA and cuDNN.
Experience integrating applications and platforms with cloud technologies, including AWS and GCP.
Strong oral and written communication skills, including the ability to lead meetings, present professionally, and explain complex technical concepts to non-technical users.

Technologies

Python, Scikit, NLTK, Numpy, Pandas, TensorFlow, Keras, R, Spark
SQL, Git, Github, CUDA, cuDNN
AWS, GCP

Role Overview

The Machine Learning Engineer 2 will be embedded within a data science organization. The position involves helping conceive, code, and deploy data science models at scale using current industry tools. Key focus areas include data wrangling, scaled model deployments, model development, and building agentic systems.

Location and Compensation

Location: Mountain View, CA (onsite). Salary: USD 140,500 to 190,000 per year.

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1mo ago

Sr Machine Learning Engineer

Uber San Francisco, CA

San Francisco, CA

1mo ago Closed

$202k - $224k/yr Full time Artificial Intelligence Big Data Data Analytics

This position is no longer accepting applications (closed Oct 20, 2026).

Job Description

Uber is hiring a Senior Machine Learning Engineer (onsite) in San Francisco to build and own machine learning models for membership offer relevance and messaging personalization across Uber and Uber Eats.

Responsibilities

Own end-to-end lifecycle for targeting and personalization models, including problem framing, data, training, offline evaluation, online experimentation, deployment, and monitoring.
Develop heterogeneous treatment effect models to estimate incremental impact of interventions on users.
Create budget-constrained allocation systems that convert per-user uplift predictions into offer decisions under real constraints, including incentive budget, variable contribution targets, cannibalization of full-price conversion, and per-surface frequency caps.
Build personalized ranking and sequencing models for membership messaging across Eats and Mobility apps, balancing conversion, user experience, and contention with non-member content.
Collaborate with backend and platform engineers to productionize models in real-time serving paths and batch pipelines, ensuring production behavior matches offline results.
Partner with Product, Engineering, Data Science, Finance, and Marketing to translate ambiguous business goals into concrete ML problem statements.

Requirements

Bachelor’s degree in Computer Science, Statistics, Economics, Operations Research, or a related quantitative field, or equivalent practical experience.
5+ years of experience building and shipping ML models that drive product or business decisions in production.
Proficiency in Python and modern ML frameworks, including PyTorch, scikit-learn, and XGBoost/LightGBM or equivalents.
Strong SQL skills and hands-on experience with large-scale data processing using Spark, Hive, Presto, or comparable tools.
Experience designing and analyzing experiments, including A/B testing, power analysis, variance reduction, and responsible interpretation of noisy results.
Experience delivering models across the full lifecycle, from notebooks to production pipelines, serving, monitoring, retraining, and deployment.
Ability to explain modeling decisions and associated business consequences clearly to both technical and non-technical audiences.

Technologies

Python
PyTorch
scikit-learn
XGBoost
LightGBM
SQL
Spark
Hive
Presto

Preferred Qualifications

Experience training deep feed-forward models (MLP) for uplift estimation.
Experience with constrained optimization for resource allocation, including LP/MIP, Lagrangian duality, dual-price, or bidding-style budget pacing.
Experience with incentive, promotion, pricing, or discount targeting at consumer scale.
Experience with contextual bandits or reinforcement learning for sequential decisioning.
Familiarity with subscription business metrics and modeling, including trial-to-paid conversion, retention curves, LTV modeling, cannibalization, and incrementality measurement.
Experience leading technical direction across an ambiguous, cross-functional scope.

Salary

USD 202,000 - 224,000 per year
For San Francisco, CA-based roles: base salary range is USD $202,000 per year - USD $224,000 per year

Benefits

Eligible to participate in Uber’s bonus program; may be offered an equity award and other types of compensation.
All full-time employees are eligible to participate in a 401(k) plan.
Eligibility for various benefits.

Work Location

San Francisco, CA (onsite)
Unless approved for full remote work, employees must spend at least 50% of their time in-office.
Some roles (including those at greenlight hubs) require full-time in-office presence.

Equal Opportunity

Uber considers qualified applicants for employment without regard to sex, gender identity, sexual orientation, race, color, religion, national origin, disability, protected Veteran status, age, or any other characteristic protected by law.
Uber also considers applicants regardless of criminal histories, consistent with legal requirements.
Accommodation: if you have a disability or special need that requires accommodation, complete the accommodation form.

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2mo ago

Machine Learning Engineer, Causal Inference, Level 5

Snapchat Los Angeles, CA

Los Angeles, CA

2mo ago Closed

$178k - $313k/yr Full time Causal Inference Causal Machine Learning Causal Ml

This position is no longer accepting applications (closed Sep 20, 2026).

Job Description

Snap is hiring a Machine Learning Engineer to build causal inference machine learning models for decision-making and experimentation.

Responsibilities

Design and develop causal impact models to quantify effects and drive value for users, advertisers, and the business
Build and productionize causal ML solutions using observational and experimental data (including uplift modeling and heterogeneous treatment effect estimation)
Plan, analyze, and interpret A/B tests and quasi-experiments, partnering with product and engineering teams to shape experimentation strategies
Assess technical tradeoffs across model complexity, bias/variance, scalability, and interpretability
Perform code reviews, uphold engineering quality standards, and contribute to scalable, maintainable infrastructure
Support rapid iteration cycles while maintaining methodological rigor

Requirements

Strong foundation in causal inference and modern treatment effect estimation approaches (meta learners, propensity score matching, instrumental variables)
Applied data science experience, including A/B testing, uplift modeling, and experimentation infrastructure
Proficiency in Python and common ML/data libraries such as pandas, NumPy, and scikit-learn
Experience solving open-ended problems with a balance of statistical thinking and engineering execution
Comfort working independently and collaborating across cross-functional teams
Strong communication and mentorship skills, including the ability to explain technical insights to non-technical partners
Education/experience qualification:
- Bachelor’s degree in computer science, statistics, economics, or a related technical field (or equivalent practical experience)
- AND 5+ years post-Bachelor’s experience in machine learning with hands-on causal inference or experimentation
- OR Master’s degree in a technical field plus 4+ years post-grad machine learning experience
- OR PhD in a relevant technical field plus 2 years post-grad machine learning experience
Demonstrated experience building causal models for product decision support and policy evaluation
Experience designing and analyzing online experiments and using causal ML in production systems

Technologies

Python
pandas
NumPy
scikit-learn
CausalM
CausalML
EconML
DoWhy

Preferred Qualifications

Advanced degree (MS/PhD) in statistics, data science, computer science, economics, or operations research
Experience with causal inference libraries such as CausalML, EconML, or DoWhy
Experience deploying models in production and working with ML or experimentation infrastructure
Deep understanding of experimentation nuances, including intent-to-treat (ITT) vs. ghost ad methodologies, and frequentist vs. Bayesian tradeoffs for decision-making under uncertainty
Experience applying causal inference to personalization, ad, or marketplace dynamics

Benefits

Paid parental leave
Comprehensive medical coverage
Emotional and mental health support programs

Work Location / Policy

Los Angeles, CA (onsite)
Default together approach: work from an office 4+ days per week

Compensation

Salary (base) ranges by zone:
- Zone A (CA, WA, NYC): $209,000-$313,000 annually
- Zone B: $199,000-$297,000 annually
- Zone C: $178,000-$266,000 annually
Equity eligibility in the form of RSUs
Starting pay may be negotiable within the salary range for the position
Compensation packages available that let you share in Snap’s long-term success

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2mo ago

Machine Learning Engineer

Stellantis Auburn Hills, MI

Auburn Hills, MI

2mo ago Closed

Full time Artificial Intelligence Big Data Data Analysis

This position is no longer accepting applications (closed Sep 6, 2026).

Job Description

Stellantis is seeking a Machine Learning Engineer / Data Scientist to build statistical models and simulations that drive Vehicle Configuration Optimization (VCO) and customer-level preference simulations for optimized Vehicle Order Guides (VOGs).

Responsibilities

Build and execute large-scale simulations (for example, 50,000 synthetic customers) to model vehicle purchase behavior
Develop statistical and machine learning models using Databricks
Convert model outputs into optimized Vehicle Order Guides (VOGs) that support product configuration decisions
Conduct exploratory data analysis and feature engineering on complex datasets
Partner closely with Data Engineering to refine and leverage curated datasets
Share insights and model recommendations with business stakeholders
Continuously evaluate and improve model accuracy and underlying assumptions

Requirements

Experience developing statistical and machine learning models with Databricks
Ability to perform exploratory data analysis and feature engineering on complex datasets
Experience building and running large-scale simulations for customer behavior modeling (e.g., synthetic customer simulations)
Experience collaborating with Data Engineering to use and improve curated datasets
Capability to communicate insights and recommendations to business stakeholders

Data & Modeling Focus

Historical vehicle sales
Competitive sales data
Feature-level willingness-to-pay data
Customer preference models

Job Details

Job ID: 2020000
Career Area: Sales & Marketing
Position Type: Salaried
Location: Headquarters & Technology Center – Auburn Hills, 48326, US (onsite)
Brand: FCA Group
Date Posted: July 22, 2026

Equal Opportunity / Accessibility

Assess candidates based on qualifications, merit, and business needs
Welcomes applications from all people regardless of sex, age, ethnicity, nationality, religion, sexual orientation, disability, or other protected characteristics
Believes diverse teams reflect its global identity and help address customer needs and future care
Benefits vary by country, norms, and legal entity
Privacy Statement provided by the respective entity whose job offer you selected