Top Software Data Science Jobs

Analyze behavioral data, evaluate product experiments, model user behavior, develop dashboards and forecasts, ensure tracking quality, and provide insights that influence product decisions. Collaborate with product and engineering teams while applying statistical, analytical, and AI methods to improve user experiences and business outcomes.

Owns end-to-end clinical research and real-world evidence data management, including research database and EDC configuration, EHR data extraction, transformation pipelines, data cleaning, quality control, data lock, statistical analysis, documentation, and automated EHR-to-EDC integrations. The role requires hands-on work with messy clinical data, scalable ETL solutions, APIs, data models, reproducible code, and cross-functional collaboration with site IT, informatics, product, and research stakeholders.

Build, evaluate, deploy, and monitor identity-resolution and record-matching models using large, noisy datasets. Design labeling and measurement strategies where no definitive answer key exists, use AI-assisted labeling, manage model retraining and lineage, review data quality, and communicate results and tradeoffs to product, engineering, and sales stakeholders. Mentor teammates and ensure responsible handling of personal data, opt-outs, retention, and lineage requirements.

Data Science Intern supporting National Life Group’s AI Center of Excellence. Responsibilities include preparing and validating datasets, developing and evaluating statistical and machine-learning models, designing experiments, creating reproducible Python and SQL workflows, prototyping generative AI applications, applying responsible AI practices, and communicating findings to technical and business audiences.

Own product experimentation and growth analytics across signup, activation, conversion, monetization, and retention. Design and analyze A/B tests, establish statistical rigor, conduct funnel, cohort, LTV, segmentation, and retention analyses, and define trusted product metrics. Partner with Product, Engineering, Growth, and executives to translate insights into product decisions and recommendations.

Lead advanced analytics initiatives using predictive modeling, time-series analysis, clustering, churn reduction, anomaly detection, recommendation systems, causal inference, and quantitative research. Translate complex findings into executive visualizations, product metrics, and actionable strategy while partnering with Product, Engineering, and Business leaders.

Provides research and data-analysis support to economists focused on monetary economics and financial markets in Asia and the Pacific. Responsibilities include preparing publications and presentations, collecting and validating data, reviewing methodologies, developing databases, identifying new data sources, applying advanced analytics, and collaborating on innovation projects. The role bridges economics, data, and technology in a multicultural international environment.

Build and deploy end-to-end statistical and machine learning solutions for clients and internal delivery teams. Responsibilities include scoping ambiguous problems, analyzing operational challenges, prototyping AI workflows, translating messy data and real-world constraints into scalable solutions, iterating with stakeholders, and developing reusable tooling and infrastructure. The role combines hands-on data science with technical consulting and requires communication with technical and non-technical stakeholders.

Architects and operates enterprise AI data platforms supporting vector search, RAG, LLM applications, and Agentic AI. Establishes data governance, lineage, traceability, explainability, and evaluation controls. Partners with engineering, cybersecurity, infrastructure, and mission stakeholders to deliver secure cloud-native capabilities. Leads technology evaluation, platform monitoring, operational reliability, and executive communication. Requires extensive experience in AI/ML platform development, distributed systems, cloud architecture, data engineering, MLOps, and enterprise data systems, along with an active Top Secret/SCI clearance with polygraph.

Support data analytics and visualization by conducting research, collecting data, applying mathematical models, algorithms, and machine learning techniques, and summarizing findings accurately. Collaborate with team members, follow department procedures, and learn actuarial methodologies, company operations, and computer systems. The intern must demonstrate strong mathematical, analytical, problem-solving, communication, teamwork, and independent work skills while pursuing an undergraduate degree in mathematics, actuarial science, or a related field.