Top Software Data Science Jobs

Designs and leads cloud-based data lake, database, ontology, ETL, and AI/ML data environments. The role integrates complex structured and unstructured data, optimizes performance, supports agentic systems, and develops AI-oriented controls and security. Responsibilities include technical architecture, stakeholder collaboration, production troubleshooting, mentoring junior engineers, and leading rapid-cycle R&D in defense or aerospace environments.

Lead and develop a team of data scientists and AI/ML engineers delivering scalable, production-grade AI solutions. Drive reusable AI products, platforms, APIs, frameworks, and enterprise architecture. Establish engineering practices including testing, CI/CD, observability, documentation, and MLOps/AIOps. Partner with product, engineering, architecture, and business stakeholders to accelerate AI adoption and transformation across global insurance operations.

Leads data science partnership for insurance pricing teams, identifying AI opportunities to accelerate product rollout. Builds evaluations for AI and agentic systems, including metrics, ground truth, statistical tests, experiments, and production testing. Measures value and risk, prototypes evaluation tools and AI applications, and translates pricing and business needs into technical requirements. Requires insurance pricing model experience, advanced statistics, Python coding, modern LLM expertise, and strong cross-functional communication.

Lead product analytics projects, define success metrics, design experiments, conduct causal and statistical analyses, and translate complex data into actionable product and business strategies. Partner with Product, Engineering, Design, Finance, Marketing, and Revenue teams to prioritize roadmaps, improve customer experiences, strengthen data foundations, and apply AI-assisted approaches to accelerate analysis and decision-making.

Leads data science for Commercial Bank strategy and analytics. Partners with data scientists, analysts, engineers, and product managers; analyzes large-scale numeric and textual datasets; develops, validates, and implements machine learning models; uses Python, cloud computing, and open-source technologies; and translates complex technical findings into business outcomes. The role also emphasizes innovation, stakeholder leadership, and talent development.

Develop and deploy machine learning models across the credit card lifecycle, including marketing, acquisitions, underwriting, and fraud prevention. Analyze large-scale numeric and textual datasets using Python, SQL, Databricks, and PySpark; perform model design, training, validation, evaluation, and implementation. Collaborate with data scientists, software engineers, and product managers to translate complex analyses into business outcomes and customer-focused financial products.

Develop and validate machine learning models for compliance and model risk management. Partner with data scientists, software engineers, and product managers to analyze large numeric and textual datasets using Python, cloud, and big-data technologies. Responsibilities span model design, training, evaluation, validation, implementation, statistical analysis, and translating technical findings into business outcomes.

Leads data science and analytical initiatives for Government Operations, including reporting, metrics, predictive modeling, AI/ML solutions, data extraction, and performance improvement. Partners with business and clinical leaders to translate needs into technical solutions, communicate insights, influence decisions, and guide junior team members. The role requires expertise in healthcare claims, government programs, Snowflake, Azure, Power BI, SQL, and operational analytics.

Leads complex data analysis, forecasting, business intelligence, visualization, data modeling, and reporting initiatives. Builds scalable analytical and data solutions, ensures data quality, translates findings into actionable recommendations, mentors others, reviews work, and supports strategic and operational decision-making.

Leads enterprise AI, analytics, and reporting initiatives across contact center transformation workstreams. Develops and optimizes AI models, establishes measurement frameworks, evaluates performance, sizes opportunities, and tracks value realization. Partners with business, product, operations, and technology teams to improve self-service adoption, advocate efficiency, contact reduction, and member experience. Prepares executive reporting and AI governance submissions while driving cross-functional adoption and continuous improvement.

Analyze advertising and product data to identify opportunities, evaluate product improvements through A/B testing, quantify financial impact, and monitor key metrics. Build dashboards, conduct root-cause analyses, and deliver actionable insights to product and leadership teams. Support roadmap planning and go-to-market strategy through experimentation, causal inference, economic modeling, and advanced analytics.

Analyze large, complex datasets from connected heavy equipment and IoT assets to identify trends, performance issues, and customer opportunities. Develop statistical and machine learning models supporting condition monitoring, fleet management, and product decisions. Partner with dealer- and customer-facing teams across business divisions to translate analytical findings into actionable recommendations. The role also involves AI/ML model deployment and applying machine, telematics, and sensor data to real-world business problems.

Develop and productionize data science solutions for aviation supply chain and fleet analytics. Build forecasting, demand-sensing, inventory optimization, telemetry ingestion, APIs, dashboards, scenario-planning tools, and machine learning lifecycle infrastructure. Ensure governance, data quality, monitoring, explainability, testing, and reproducibility. Collaborate with cross-functional stakeholders to measure business impact, maintain production software, and mentor engineers and analysts.

Develop and lead complex AI and data science solutions, shape AI technical strategy, research emerging technologies, resolve large-scale technical problems, and guide cross-functional teams. The role focuses on scalable data solutions, machine learning, deep learning, large language models, generative AI, NLP, computer vision, Python, and Vertex AI. It requires a bachelor's degree and at least five years of progressive data analytics and data science experience.

Partner with a business area to answer complex causal questions, design experiments, define success metrics, and translate analysis into measurable decisions. Build reusable data products and AI workflows, support product and growth analytics, evaluate AI agents, and communicate technical findings clearly to executives. The role requires production SQL and Python, experimentation and causal inference experience, strong statistical reasoning, and effective use of AI tools.