• Experimentation & Causal Inference: Design and implement scalable A/B tests to train causal inference models and multi-armed bandits to optimize registration, trial conversion, and retention strategies. • Automation & Personalization: Build and scale automation tools for experimentation and segmentation, ensuring teams can efficiently test and iterate on hypotheses. • Growth Optimization: Develop predictive models and statistical frameworks to improve trial-to-subscription conversion, reduce churn, and enhance overall customer LTV. • User Segmentation & Personalization: Leverage clustering, behavioral analysis, and geospatial data to identify key user segments and tailor experiences accordingly. • Product Development: Collaborate with product managers and engineers to conceive and develop innovative product ideas, driving feature iteration and development to make impact. • Cross-functional Collaboration: Partner closely with growth, lifecycle marketing, and user acquisition teams to translate insights into scalable marketing and product strategies. • Data Infrastructure & Tooling: Work with data engineering to improve the data ecosystem, ensuring robust, scalable pipelines for experimentation and analysis. • Thought Leadership: Act as a mentor and thought leader within the data science team, helping to define best practices in experimentation, machine learning, data-driven decision-making, and agentic AI (AI-Native) best practices.

• Study Design & Execution: Lead a longitudinal, quasi-experimental study starting May 7 to measure clinical outcomes, specifically hospitalization frequency and discharge follow-ups. • Causal Inference Modeling: Apply advanced methodologies (e.g., propensity score matching) to observational data to estimate counterfactual patient outcomes with minimal bias. • Actuarial-Grade Validation: Develop and refine statistical models that will be thoroughly vetted and approved by customer actuaries. • Stakeholder Management: Serve as the primary analytical face to SCMG, gathering requirements and aligning on clinical/business definitions of success. • Data Storytelling: Translate complex statistical findings into compelling presentations and client-ready reports for both technical and non-technical leadership. • Vendor Audit & Wrap-up: Extract usable value from an existing outsourced study, close out the vendor contract, and integrate relevant findings into the final study. • Technical Infrastructure: Navigate and build analytics reporting infrastructure using SQL, Python, dbt, Redshift, and Looker. • Project Handoff: Ensure all code is clean and reproducible for final handover to the internal Phamily BI team.

Nebius Academy (staffed by TripleTen) is an independent educational and research division of Nebius, a global AI infrastructure company. We create accessible learning programs and foster research collaborations at the intersection of AI and emerging technologies. Our team works across locations and backgrounds, united by the goal of empowering talent to build, understand, and apply advanced AI systems. We collaborate with top academic institutions and industry leaders, and operate with the mindset of a product-driven, impact-oriented team — valuing initiative, curiosity, and clarity of execution. We are looking for a Senior Data Scientist to support the development and validation of scenario-based assessments for hiring. The role includes designing evaluation logic and metrics, analyzing candidate and assessment data, partnering with product/engineering/psychometrics, and improving the reliability and scalability of the SkillCheck experience. What you will do - Design and analyze scenario-based and AI-supported assessment data for hiring and skill evaluation. - Develop evaluation logic, metrics, and analytical approaches to measure candidate performance and assessment quality. - Partner with product, psychometrics, and engineering teams to improve the reliability, validity, and scalability of the assessment experience. - Run analyses on candidate behavior, outcomes, and assessment signals to generate product and hiring insights. Requirements - 4+ years of experience in data science or quantitative analysis - Proficiency in Python and SQL - Experience designing evaluation logic, metrics, and analytical frameworks - Strong experimentation mindset and experience running A/B tests - Ability to analyze behavioral and outcome data to generate actionable insights - Experience working cross-functionally with product and engineering teams - Familiarity with assessment quality, validity, or psychometrics is a plus - Strong communication skills: ability to translate data findings into product and business insights What we can offer you - Salary 6,500–8,500 USD / month gross - Full-time remote contract with a flexible schedule - A close-knit, international team of thoughtful and driven professionals - All software licenses and remote work tools you need to be successful in a remote working environment (Slack, Miro, Notion, Zoom, etc) - Room for growth as we scale our portfolio of educational products and formats - Complete autonomy over B2B product vision and roadmap - Direct collaboration with cutting-edge AI infrastructure technology

• End-to-end ownership of Search, Recommendations, and Personalization • Identify high-impact opportunities • Rapidly prototyping and experimenting (often using AI tools) • Measuring and iterating based on real business outcomes • Develop and iterate on models (ranking, recommendation, personalization, etc.) • Partner with Product, Engineering, and Analytics to bring ideas to life