Top Senior Data Science Jobs

Leads the design, development, validation, deployment, and monitoring of statistical and machine learning models. Translates business needs into analytical solutions, analyzes complex datasets, communicates insights to technical and non-technical stakeholders, manages data science projects, resolves data and model issues, and mentors junior team members. Partners cross-functionally to deliver scalable, data-driven recommendations and promote governance, responsible AI, and best practices.

Develop and enhance predictive models for search and recommendations using large datasets, statistical methods, and machine learning. Incorporate new data sources, design controlled experiments, communicate insights to senior leadership, research emerging methodologies, and help shape the data science roadmap. The role also requires collaboration, project delivery, and explaining technical concepts to technical and non-technical audiences.

Develop and maintain linear and nonlinear credit risk models for consumer lending. Responsibilities include feature engineering, dataset creation, exploratory and ad hoc analysis, production deployment, monitoring, validation, and translating business needs into data science problems. The role provides actionable recommendations, influences team strategy, collaborates cross-functionally, and independently drives projects from concept through completion.

Lead the technical strategy for search and recommendation systems, develop models, and improve business outcomes through data science expertise.

Develop actionable insights for autonomous-driving AI engineering teams through experimental and observational analysis of real and simulated driving. Define performance metrics, design experiments, evaluate statistical results, investigate model training and inference bottlenecks, and identify improvements to functionality, safety, and performance. The role also involves building large datasets with SQL, applying statistical and machine-learning methods, communicating findings, and influencing engineering priorities and strategy.

Own the end-to-end development of recommendation and personalization models for a large commerce catalog. Design embedding, retrieval, ranking, and cold-start strategies; conduct offline evaluations and online experiments; create features from behavioral and catalog data; apply NLP, computer vision, and LLM techniques; and deploy, monitor, and improve production models on the existing MLOps platform.

Develop automated analytics products and advanced data science solutions using machine learning, NLP, statistical modeling, predictive analytics, and generative AI. Lead end-to-end workflows from data preparation and experimentation through model validation, deployment, and visualization. Partner with product, engineering, and platform teams to move solutions from research into production, communicate findings to technical and non-technical audiences, and guide other data scientists.

Develop and deploy scalable data science products using machine learning, generative AI, NLP, statistical modeling, and predictive analytics. Analyze complex datasets, manage end-to-end modeling workflows, communicate insights through dashboards and presentations, and partner with engineering teams on production deployment. Provide technical guidance, apply responsible AI and model governance practices, and evaluate emerging technologies for business and clinical outcomes.

Leads the design, development, and deployment of advanced data science, machine learning, generative AI, and agentic AI solutions. Builds scalable analytics products, RAG systems, and evaluation frameworks; establishes technical standards and responsible AI governance; mentors data scientists; and partners with engineering teams to deploy and monitor production solutions. Translates complex analyses into actionable clinical, operational, and economic insights while guiding technical strategy and client-focused initiatives.

Lead end-to-end data science engagements combining statistical modeling, machine learning, large-scale data transformation, and generative AI. Build agentic LLM applications, present analytical solutions to C-level stakeholders, lead workstreams, mentor junior data scientists, and collaborate with delivery and account teams. Develop reusable documentation and code libraries while contributing to internal research and education.

Lead predictive modeling and causal inference initiatives for clients, developing models from exploratory analysis through production deployment, monitoring, and retraining. Apply GLMs, econometric methods, uplift modeling, propensity methods, and deep-learning forecasting. Process large-scale data with SQL and Spark, build models in Python, improve production reliability, and communicate findings to technical and business stakeholders in a consulting environment.

Build marketing data science models and decision systems for spend allocation, channel measurement, forecasting, experimentation, and growth optimization. Unify marketing, retail, revenue, and product data; develop dashboards and analytical products; apply AI/ML and causal inference; and translate complex analysis into actionable recommendations for executives and cross-functional teams. The role also includes improving data quality, mentoring, documentation, and technical leadership.

Improve Entrata’s AI models through foundation-model fine-tuning, training-data curation, evaluation frameworks, benchmarking, experimentation, and error analysis. The role translates business problems into measurable machine-learning objectives, assesses model safety and reliability, and partners with machine-learning engineers to productionize successful approaches. Responsibilities include working with instruction, preference, and synthetic datasets and communicating findings to technical and business stakeholders.

Lead data science projects across R&D, manufacturing, sales, and supply chain. Prepare and clean data, build regression and machine learning models (random forest, xgboost, lasso), perform cross-validation and hyperparameter tuning, query data with SQL, create plots and reports, communicate results to nontechnical stakeholders, identify high-value problems, and collaborate with data engineering and software teams to deploy models.

Lead data science work embedded in the newsroom: identify high-impact problems, build and productionize predictive and explanatory models, own core metrics/measurement, apply causal inference and experimentation, translate findings for senior stakeholders, and mentor junior data team members to improve analytic standards and adoption.

The Sr. Data Scientist will utilize statistical and machine learning techniques to solve healthcare-related problems and enhance predictive modeling across products, collaborating closely with various teams.

The Senior Data Scientist will design and implement machine learning solutions for streaming data, troubleshoot production issues, and develop data processing pipelines, leveraging significant knowledge in AI and network communication.

The Senior Data Scientist, Marketing will develop analytical models and reporting solutions to support user acquisition and marketing effectiveness, utilizing AI for improved marketing insights.

Leads data science and advanced analytics initiatives for supply chain and plant operations. Develops machine learning, forecasting, optimization, and simulation models; translates complex operational challenges into actionable insights; and influences strategic business decisions. Partners with business leaders, evaluates emerging AI technologies, establishes analytical best practices, monitors model performance, communicates findings, and mentors data scientists and analysts.

Lead product data scientist shaping growth strategy, experimentation, and metrics. Build self-service analytics, size opportunities, design and analyze experiments with product and engineering, drive activation/engagement/monetization insights, and evolve the company's experimentation culture.

Lead a data science team to build scalable analytics and predictive models for pricing decisions. Partner with business and engineering to analyze large datasets, prioritize work, ensure model quality, and present data-driven optimization recommendations.

Lead analytics for user setup and onboarding: build dashboards, pipelines, alerts, and statistical models; run A/B tests and EDA; partner with product and engineering to drive adoption and provide data-driven recommendations to leadership to improve onboarding at scale.

Lead analytics for user onboarding: build dashboards and pipelines, run A/B tests and statistical models, analyze trends, create real-time alerts, and provide data-driven recommendations to product and engineering leadership to improve onboarding at scale.

Drive data-science for people- and business-facing products: establish experimentation and instrumentation best practices, apply statistical and ML methods to web-scale data, build scalable analysis pipelines, and partner cross-functionally to turn insights into product improvements.

Lead a small-to-medium data science team to design, develop, and deploy advanced AI/ML solutions (especially LLMs/Transformer models), mentor junior staff, define methodologies and governance, collaborate cross-functionally, and set team strategy and high standards for model development.