Top Senior Data Science Jobs

Lead product analytics for Handshake's recruiting marketplace: define metrics, own analytics strategy, run experiments, evaluate ML models, perform cohort and funnel analyses, build dashboards, and partner with product, engineering, and analytics engineering to drive data-informed growth and pricing decisions.

Lead development, validation, deployment, and optimization of predictive models and analytics. Partner with claims, underwriting, actuarial teams to transform complex datasets into actionable insights, ensure data quality and compliance, and communicate results through visualizations. Advance data science capabilities across the organization.

Turn data into product and business decisions through metric design, experimentation, causal analysis, data modeling, deep-dive analysis, dashboards, and data storytelling. Partner with Product, Finance, GTM, Growth, and Engineering leaders to measure impact, influence priorities, and improve company-wide data practices. Model analytical data in dbt, collaborate with data engineering, and help shape an AI-first data team.

Build statistical and machine learning models that improve content discovery, user engagement, and retention. Apply causal inference, experimental design, and A/B testing to measure product impact and validate changes. Collaborate with ML engineers to deploy models, translate analysis into product decisions, and develop features using text, multimodal embeddings, and GenAI. Analyze large-scale data and recommender systems to drive measurable improvements in a consumer product serving millions of users.

Leads end-to-end machine learning model development, from business problem definition and data exploration through deployment, monitoring, documentation, and retraining. Mentors data scientists, collaborates with stakeholders and product teams, troubleshoots production code and ML pipelines, and guides Agile delivery. Requires advanced expertise in statistical modeling, machine learning, cloud platforms, MLOps, production coding, large datasets, containers, version control, and data lakes.

Develop and maintain data analytics solutions, predictive models, and machine learning algorithms to improve LNG production efficiency and reduce safety and downtime risks. Analyze data lake and warehouse data, define ETL and architecture requirements, support governance and testing, collaborate with business stakeholders, document solutions, and mentor junior data scientists. The role also contributes to data science lifecycle standards, technology stack development, and strategic planning.

Develops and deploys predictive analytics, machine learning, and deep learning solutions for production use. Responsibilities include statistical analysis, feature engineering, model development, anomaly detection, NLP, computer vision, MLOps, CI/CD pipelines, cloud AI platform integration, data engineering collaboration, model monitoring, governance, and stakeholder communication. The role also supports large-scale analytics, AI modernization, and human-reviewed multi-source summarization.

Leads applied data science initiatives involving Python, NLP, LLMs, and end-to-end machine learning workflows. Explores, cleans, analyzes, visualizes, and mines large-scale data; designs models for document classification, extraction, summarization, and search; and deploys solutions in production streaming and data lake environments. Collaborates with clients, MLOps, and infrastructure teams while guiding teammates and supporting model deployment, monitoring, and retraining.

Develop and maintain large housing datasets and infrastructure for US housing policy research. Responsibilities include data acquisition, cleaning, analysis, modeling, automation, visualization, dashboard development, web scraping, API use, quality control, and publication. Senior responsibilities add predictive modeling, machine learning, workflow supervision, project management, and establishing data-management best practices.

Develop and deploy production-grade AI and data science solutions for scientific discovery, including LLM applications, NLP, semantic search, RAG, agentic workflows, knowledge graphs, and recommendation systems. Lead model evaluation, experimentation, monitoring, and lifecycle management while collaborating with engineering, product, research, and domain teams. Influence technical strategy, mentor data scientists, and deliver reliable, scalable, evidence-grounded AI products.

Own the analytical layer for autonomous-driving model evaluation and release decisions. Analyze simulation and on-road experiment data, improve performance metrics and scoring methods, identify measurement gaps, apply statistical rigor, and translate investigations into actionable recommendations. Partner with Data Science, Validation, Product, engineering, and OEM teams to strengthen evaluation coverage, ground truth, quality investigations, and weekly model-release feedback loops.

The Senior Associate, Data Scientist will partner with cross-functional teams to identify risks associated with models and validate machine learning models across the enterprise, enhancing decision-making while ensuring model governance and risk management.

Own applied data science for content performance, audience behavior, attribution, segmentation, and pilot performance prediction. Build models for engagement, sentiment, conversion, retention, and campaign attribution; track forecast accuracy and calibration; and translate findings into actionable recommendations. The role also mentors a junior analyst, strengthens statistical rigor, and partners with research and data science leadership.

Build and deploy data science models for demand and sales forecasting, promotion effectiveness, pricing, and retail analytics. Analyze messy datasets from sell-through, inventory, deductions, trade promotions, and supply-chain signals. Develop statistical quality checks, evaluate model performance, quantify uncertainty, and collaborate with AI/ML engineers to ship production-grade insights within the product.

Develops data science algorithms and systems to analyze structured and unstructured data, apply supervised and unsupervised AI training and NLP, manipulate data from multiple platforms, and create visualizations that help clients make informed decisions. The role requires collaboration with clients, five or more years of analytical or statistical programming experience, a Secret clearance, and a relevant bachelor's degree.

Leads end-to-end data science and AI solutions for service and operations analytics, including predictive modeling, forecasting, recommendation engines, GenAI, deployment, monitoring, and governance. Partners with stakeholders to define problems, deliver actionable insights, influence strategy, and drive measurable business impact. Provides technical leadership, advances analytics practices, mentors peers, and supports scalable production solutions.

Leads advanced analytics and AI initiatives supporting service and operations strategy. Designs, deploys, monitors, and improves machine learning, forecasting, predictive modeling, GenAI, and recommendation-engine solutions. Partners with stakeholders to frame complex problems, communicate insights, and drive measurable business outcomes. Ensures responsible AI governance, mentors peers, and advances scalable data science practices across the organization.

Develops statistical and machine-learning models for pricing strategy, revenue optimization, demand forecasting, segmentation, and utilization analysis. Designs experiments, validates and documents models, prepares large-scale datasets, and partners with data engineering teams on scalable pipelines and deployment. Translates analytical outputs into pricing recommendations, dashboards, and decision tools while supporting governance, stakeholder presentations, and analyst enablement.

Develop and optimize machine learning and statistical models for fraud detection and anti-money laundering. Analyze large datasets to identify fraudulent patterns, build scalable data pipelines and features, evaluate model performance, and collaborate with engineering, product, and operations teams. Communicate insights to technical and non-technical stakeholders while maintaining data privacy and security compliance. The role offers significant ownership in building Oscilar’s early machine learning stack within a remote-first startup.

Leads data science initiatives by exploring and mining data, developing advanced AI, machine learning, and statistical models, and translating complex findings into strategic business actions. Presents insights to stakeholders and senior leaders, guides solutions from ideation through delivery, coaches less-experienced professionals, and collaborates with business clients to understand needs. The role is hybrid in Columbus, Ohio, with occasional emergency-response support potentially outside normal hours.

Own the optimization and decision science capabilities for Crucible, Firestorm’s manufacturing planning software. Develop production, sourcing, inventory, scheduling, allocation, and network optimization models; address uncertainty; validate and deploy production-ready solutions; and collaborate with software, manufacturing, supply chain, and product teams. Set technical direction, establish modeling standards, and build the foundation for future data science efforts.

Conducts healthcare data collection, validation, analysis, reporting, dashboard development, root-cause analysis, program evaluation, predictive modeling, and AI/ML work. Collaborates with stakeholders to define requirements, improve business outcomes, connect complex healthcare data sources, and communicate findings through reports, visualizations, and recommendations. Manages multiple projects while delivering accurate, timely results.

Lead advanced analytics initiatives for operations, including lakehouse data integration, maintenance and work-order analytics, asset reliability modeling, inventory forecasting, anomaly detection, and NLP of technician notes. Build Databricks Apps and production ML workflows using Python, PySpark, SQL, MLflow, and Databricks technologies. Partner directly with operations stakeholders to translate business knowledge into data definitions and self-service analytical tools.

The Senior Data Scientist will lead research using historical data, solve market-impact problems, design research tools, apply AI/ML in finance, mentor others, and integrate models into production systems.

Design, build, evaluate, and productionize agentic AI, LLM, RAG, and predictive ML systems for scientific information products. Develop training and inference pipelines, fine-tune models, establish trustworthy-AI evaluation and safeguards, and operate AI capabilities at scale. Set engineering standards, lead technical initiatives, mentor scientists and engineers, influence product and executive decisions, and communicate results through presentations, publications, and client engagements.