Top Software Data Science Jobs

Build actuarial and health-economic models (total cost of care, PMPM, MLR) from claims to project multi-year cost and utilization impacts, quantify uncertainty, support commercial pricing, defend analyses to payer actuaries, partner on causal/expt design, and produce outputs usable for pricing/reserving.

Analyze and characterize conversational audio data, identify underrepresented areas, and build active-learning and human-in-the-loop workflows. Develop representative benchmarks, automate model adaptation, and create reusable data analysis and scoring systems. Partner with research, engineering, and data teams to run experiments, improve models, organize data, and translate complex findings into measurable product improvements.

Leads a data science team and personally drives analyses, experimentation, and product analytics to identify patient experience improvements. Partners with Product, Engineering, Research, and Data Engineering teams to develop product features, evaluate business and product performance, guide product planning, and translate statistical findings into actionable recommendations. Manages and develops analytical talent while applying data storytelling, A/B testing, marketplace metrics, and retention, conversion, engagement, and churn analysis.

As a Senior Data Scientist, you will develop technologies for signals exploitation, perform data analysis, and implement algorithms while collaborating with project teams.

Lead Data Scientist will define goals and metrics for algorithm Test & Evaluation, research T&E methodology, lead vendor coordination, present analyses and results to stakeholders, and transition algorithms into software and real-time embedded systems.

The Manager of Data Science will lead a team focused on machine learning model development, team management, and cross-functional collaboration to advance product goals.

Lead end-to-end data science projects involving causal inference, experiments, statistical modeling, machine learning, political behavior, and online narratives. Build scalable data pipelines and production-quality Python code from messy datasets, rigorously evaluate models, collaborate with researchers and engineers, and contribute methods and technical direction. Mentor junior modelers while translating ambiguous questions into practical, deployable solutions.

The Data Scientist I will clean and structure messy datasets, engineer features, run exploratory and numerical analyses, fit and evaluate statistical and machine learning models, and support experiments. The role involves writing documented Python code, monitoring model performance, and collaborating with researchers and engineers. This early-career position offers mentorship while progressively increasing ownership of modeling projects involving online narratives, public opinion, AI, machine learning, and causal inference.

Tavern Research welcomes expressions of interest from curious, adaptable candidates interested in building technology at the intersection of data science, politics, and media production. Applicants should be comfortable solving difficult problems, learning quickly, using emerging tools, and performing under pressure. Political experience is helpful but not required. Employees are expected to work regularly in person from the company’s Chicago office.

Develops Copilot usage and quality metrics, product insights, instrumentation, and large-scale A/B experiments. Analyzes telemetry with statistical and machine-learning methods, builds data pipelines, evaluates feature performance, and communicates data-driven recommendations to leadership and cross-functional teams. Requires expertise in product analytics, experimentation, complex data, algorithms, and LLMs.

Leads DAT’s Marketplace and Decision Optimization team of applied scientists. Responsibilities include hiring and developing scientists, setting technical roadmaps, building recommendation, auction, negotiation, pricing, and monetization models, and advancing experimentation and causal inference practices. The manager remains hands-on with modeling, experiments, and analysis while influencing product, engineering, finance, and go-to-market decisions across DAT’s marketplace and subscription businesses.

Analyzes complex structured and unstructured data, develops statistical, predictive, prescriptive, NLP, and machine learning models, and builds data pipelines and automated processing environments. The role uses Python, R, distributed data technologies, cloud platforms, and visualization tools to support U.S. Special Operations Command decision-making, digital transformation, and mission readiness. It also requires communicating analytical findings to nontechnical stakeholders and collaborating with data engineers and command personnel.