Statistical Modeling

Statistical Modeling Engineer Jobs

7 jobs found

Today

Sr Machine Learning Engineer

Uber San Francisco, CA

San Francisco, CA

Today

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

Apply Now

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.

View full job page →

30d ago

Machine Learning Engineer, Causal Inference, Level 5

Snapchat Los Angeles, CA

Los Angeles, CA

30d 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

View full job page →

1mo ago

Machine Learning Engineer

Stellantis Auburn Hills, MI

Auburn Hills, MI

1mo 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

View full job page →

1mo ago

Machine Learning Engineer

Photon New Jersey

New Jersey

1mo ago Closed

Full time Python Advanced Analytics Ai Ml

This position is no longer accepting applications (closed Aug 30, 2026).

Job Description

Photon offers an onsite role in New Jersey that emphasizes collaboration, real-world impact, and continuous growth. As a Machine Learning Engineer, you will design and deploy analytics models that address concrete business challenges, partner with product and engineering teams to deliver end-to-end ML solutions, and push models into production. This position centers on practical application of statistics and machine learning using Python, Spark, and Databricks, with the opportunity to influence customer experiences and key business decisions.

Responsibilities

Analyze business use cases and craft analytics models using appropriate statistical and machine learning techniques tailored to specific needs.
Develop machine learning algorithms to personalize customer experiences and generate actionable insights.
Apply data mining and machine learning methods, including forecasting, prediction, segmentation, recommendation, and fraud detection.
Augment company data by integrating third-party sources to expand analytics capabilities.
Enhance data collection processes to capture information essential for analytics systems.
Prepare raw data for analysis by cleaning data, imputing missing values, and standardizing formats using Python data frameworks such as Pandas and NumPy.
Implement machine learning models with a focus on performance and scalability using PySpark in Databricks.
Design and build infrastructure to enable large-scale data analytics and experimentation.
Utilize tools like Jupyter Notebooks for data exploration and model development.

Requirements

Undergraduate or graduate degree in Computer Science, Mathematics, Physics, or related fields; PhD preferred but not necessary.
At least five years of experience in data analytics with a solid grounding in core statistical algorithms such as classification and regression.
Strong experience with Python based machine learning libraries including scikit-learn, TensorFlow, and PyTorch.
Proficiency with analytics platforms like Databricks for large-scale data processing.
At least four years of continuous experience with Spark, with emphasis on PySpark.
Hands-on experience with data processing and analysis tools such as Pandas, NumPy, and Jupyter Notebooks.

View full job page →

1mo ago

AI Engineer

Wayy Research Remote 🌍 Remote

Remote

1mo ago Closed

$60 - $70/hr Full time Marketing Python Algorithms

This position is no longer accepting applications (closed Aug 29, 2026).

Job Description

Develop and own algorithms for budget allocation, attribution, and rigorous experimental measurement in a remote AI engineering role.

Responsibilities

Build and maintain a multi-armed bandit engine using Thompson Sampling on Beta-Bernoulli reward models, with contextual extensions as needed; tune exploration versus exploitation, handle cold-start scenarios, and monitor non-stationary reward drift.
Implement attribution with Shapley value for multi-touch credit assignment and compare against first-touch, last-touch, and position-based baselines.
Design and operate experiment infrastructure for A/B and multivariate tests, including power analysis, sequential testing with proper alpha spending, and significance testing that remains valid under peeking.
Conduct cross-channel causal measurement using geo holdouts, incrementality tests, and difference-in-differences designs to isolate true interaction effects from correlation.

Requirements

Strong Python skills with NumPy, SciPy, pandas, and scikit-learn.
Hands-on experience with multi-armed bandits including Thompson Sampling, UCB, and epsilon-greedy; understands regret bounds beyond API usage.
Expertise in hypothesis testing and experimental design: power analysis, multiple comparisons correction, sequential testing, and the peeking problem.
Production-scale A/B testing experience; you have shipped tests, not only analyzed them.
Proficiency in causal inference methods such as DiD, synthetic control, instrumental variables, or incrementality testing.
Advanced SQL skills with PostgreSQL.

Technologies

Python
NumPy
SciPy
pandas
scikit-learn
PostgreSQL
PyMC
Stan
NumPyro

Benefits

Pay: $60.00 - $70.00 per hour
Remote

You’ll fit if

You expect a control group before reporting lift numbers and can explain the difference between correlation and causation to a CMO without condescension.

Strongly preferred

Marketing mix modeling or media measurement background
Bayesian methods with PyMC, Stan, or NumPyro
Shapley values or cooperative game theory applied to attribution
Production ML deployment experience, not just notebooks

View full job page →

1mo ago

Modeling and Simulation Engineer - Data Analytics (Engineer or Principal Engineer Level)

Northrop Grumman San Diego, CA

San Diego, CA

1mo ago Closed

$92k - $171k/yr Full time Senior Analytics Business Intelligence

This position is no longer accepting applications (closed Aug 29, 2026).

Job Description

Modeling and Simulation Engineer - Data Analytics at Northrop Grumman Aeronautics Systems in San Diego, CA, focuses on designing data mining pipelines, performing statistical analysis, and building dashboards to support data-driven decisions within the Sustainment Systems Engineering team on the Triton Enterprise. This role is offered at Engineer or Principal Engineer level with a salary range of USD 91,800 to 171,000 per year.

Responsibilities

Data Architecture & Modeling – Design scalable data mining pipelines, establish protocols for data ingestion, cleaning, transformation, and storage. Evolve models to capture long term trends and support real time analytics.
Statistical Insight – Perform exploratory data analysis, hypothesis testing, and advanced statistical modeling to identify key drivers and associations within heterogeneous data sources.
Visualization & Reporting – Create interactive dashboards and reports using modern BI and visualization tools (e.g., MATLAB, Tableau, Power BI, Plotly) to communicate insights to technical and non technical stakeholders.
Cross Functional Collaboration – Partner with engineering, operations, and business units to define problem statements, translate requirements into analytical solutions, and integrate findings into product roadmaps.
Innovation & Knowledge Leadership – Stay abreast of cutting edge data science methodologies (e.g., deep learning, reinforcement learning) and evaluate their relevance to defense contexts. Mentor teams and promote a culture of data driven decision making.
Governance & Quality – Establish data governance standards, data quality metrics, and reproducibility practices across the analytics lifecycle.

Requirements

Must have a Bachelors of Science degree in a STEM field and at least 2 years of relevant military / professional experience, OR a Master's Degree in a STEM field and at least some of relevant military / professional / academic experience
Must have a Bachelors of Science degree in a STEM field and at least 5 years of relevant military / professional experience, OR a Master's Degree in a STEM field and at least 3 years of relevant military / professional experience, OR a PhD and at least 1 year of relevant military / professional / academic experience
Must have an active US Government Secret or higher clearance (with a background investigation completed within the last 6 years or currently enrolled into Continuous Evaluation)
Must have the ability to obtain and maintain Special Access Program (SAP) clearance within a reasonable amount of time as determined by business needs
Must have experience in ingesting, processing, storing, analyzing and visualizing large data sets

Technologies

Python
SQL
Tableau
MATLAB
Power BI
Plotly
.DAT file decoding

Benefits

Health insurance coverage
Life insurance
Disability insurance
Savings plan
Company paid holidays
Paid time off (PTO) for vacation and/or personal business
Long Term Incentives

Relocation Assistance

Relocation assistance may be available

Clearance Required for Start

Yes

Clearance Type

Secret

Travel

Yes, 10% of the Time

View full job page →

1mo ago

Modeling and Simulation Engineer - Data Analytics

Northrop Grumman San Diego, CA

San Diego, CA

1mo ago Closed

$92k - $138k/yr Full time Analyst Analytics Big Data

This position is no longer accepting applications (closed Aug 27, 2026).

Job Description

This onsite role, Modeling and Simulation Engineer - Data Analytics, is based in San Diego, CA, and focuses on designing data mining pipelines, performing statistical analyses, building dashboards, and supporting data-driven decision making within the Triton Enterprise Sustainment Systems Engineering program for Northrop Grumman Aeronautics Systems.

Responsibilities

Data Architecture & Modeling – Design scalable data mining pipelines, establish ingestion, cleaning, transformation, and storage protocols, and evolve models to capture long term trends to support real-time analytics.
Statistical Insight – Conduct exploratory data analysis, hypothesis testing, and advanced statistical modeling to identify key drivers across heterogeneous data sources.
Visualization & Reporting – Develop interactive dashboards and reports using tools such as MATLAB, Tableau, Power BI, and Plotly to communicate insights to both technical and non-technical stakeholders.
Cross Functional Collaboration – Partner with engineering, operations, and business units to define problem statements, translate requirements into analytical solutions, and integrate findings into product roadmaps.
Innovation & Knowledge Leadership – Stay current with cutting edge data science methodologies including deep learning and reinforcement learning, assess their relevance to defense contexts, mentor teams, and promote a data-driven decision making culture.
Governance & Quality – Establish data governance standards, data quality metrics, and reproducibility practices across the analytics lifecycle.

Requirements

Bachelor of Science in a STEM field with at least 2 years of relevant military or professional experience, OR a Master’s Degree in a STEM field with some relevant military, professional, or academic experience.
Bachelor of Science in a STEM field with at least 5 years of relevant military or professional experience, OR a Master’s Degree in a STEM field with at least 3 years of experience, OR a PhD with at least 1 year of relevant military, professional, or academic experience.
Active US Government Secret or higher clearance with a background investigation completed within the last 6 years or currently enrolled in Continuous Evaluation.
Ability to obtain and maintain Special Access Program (SAP) clearance within a timeframe determined by business needs.
Experience ingesting, processing, storing, analyzing, and visualizing large data sets.

Technologies

MATLAB
Tableau
Power BI
Plotly
Python
SQL
.DAT file decoding

Benefits

Health insurance coverage
Life and disability insurance
Savings plan
Company paid holidays
PTO for vacation and/or personal business
Annual bonuses
Long Term Incentives
Discretionary bonus

Relocation Assistance

Relocation assistance may be available

Clearance Required for Start

Yes

Clearance Type

Secret

Travel

Yes, 10% of the Time

Salary

Primary level salary range: $91,800.00 - $137,600.00 per year

Secondary level salary range: $114,000.00 - $171,000.00 per year

View full job page →

Statistical Modeling Engineer Jobs

Sr Machine Learning Engineer

Machine Learning Engineer, Causal Inference, Level 5

Machine Learning Engineer

Machine Learning Engineer

AI Engineer

Modeling and Simulation Engineer - Data Analytics (Engineer or Principal Engineer Level)

Modeling and Simulation Engineer - Data Analytics

Get Statistical Modeling Engineer Jobs alerts