Top Software Data Science Jobs

Lead and hands-on manage a product Data Science team focused on app experience and data marketplace. Drive AI-native deliverables (agentic services, semantic views), validate AI/ML and LLM outputs, build POCs, mentor teammates on DS and governed generative AI usage, partner with product and engineering stakeholders, and grow team impact and hiring.

Own the complete data science lifecycle for a flagship predictive modeling project. Responsibilities include analyzing and engineering multi-source data, building production-grade pipelines, developing and deploying supervised, unsupervised, reinforcement, deep learning, NLP, and forecasting models, monitoring performance, documenting work, communicating insights, and mentoring junior team members.

Lead data science for defense clients: perform data engineering, mining, exploratory/predictive/statistical analysis; build ML/AI tools (recommendation engines, automated scoring); manage large, disparate data sources using R/Python/SQL; create visualizations in Power BI/Tableau; support Multi-INT analytics and provide senior-level analytic products for the defense cover program.

Build and own the analytics pipeline and semantic layer, define canonical product and business metrics, evaluate AI/agent performance, support pricing and profitability analysis, design dashboards and quality checks, translate ambiguous questions into analyses, and hire/scale the data team.

Provide analytics and data-driven insights for CoverMyMeds clients, prioritize work that drives commercial outcomes, build relationships with account teams, apply statistical and predictive methods, monitor portfolio health, deliver client-facing visuals and recommendations, support complex requests and escalations, and inform strategic decisions across product, analytics, and operations.

Lead a team of 6–10 data scientists to drive user acquisition, activation, retention, and reengagement through experimentation, forecasting, ML, and business intelligence. Set analytical vision, build forecasting systems and metrics, embed data scientists in product teams, influence leadership with clear narratives, and hire and develop high-performing talent.

Develop and maintain sustainability methodologies that translate climate science, GHG accounting standards, and regulatory requirements into auditable product logic. Collaborate with engineers, data scientists, product managers, customers, auditors, and stakeholders; review code, write technical specifications, contribute to implementations and testing, investigate complex sustainability data questions, and document scientific decisions. The role applies sustainability expertise to real-world business data and product capabilities.

Own high-impact data science and operational decision problems across pricing, routing, fleet utilization, charging, experimentation, and network planning. Build machine learning, statistical, optimization, forecasting, and simulation models; design experiments and causal studies; partner with engineering to productionize decision systems; monitor model and KPI performance; and communicate recommendations that improve reliability, utilization, customer experience, and unit economics.

Own the end-to-end data science function for a business area such as growth, product, AI and automation, or strategic partnerships. Define business problems, metrics, reports, analyses, and experiments; generate actionable insights; and influence leadership decisions. The role requires independently structuring ambiguous problems, using SQL or Python, communicating findings clearly, and driving measurable business outcomes.

Own Data, Cloud & AI coverage using a proprietary dataset to produce forward-looking, data-driven research. Analyze trends across AI/ML operations, databases, data integration, governance and cloud infrastructure; produce client-facing insights, thought leadership, and collaborate with product and engineering to improve research methodology and platform fidelity.