• Advise research and engineering teams on statistical methodologies, experimental design, predictive modeling, and analytical best practices. • Help improve AI model performance across data science, analytics, and machine learning domains. • Create complex, real-world data science tasks that test quantitative reasoning, statistical thinking, and machine learning knowledge. • Develop accurate, well-structured solutions that reflect industry best practices. • Review and assess analytical solutions generated by AI systems and subject matter experts. • Identify errors, inconsistencies, flawed assumptions, and opportunities for improvement. • Provide clear, actionable feedback to enhance model quality and reasoning capabilities. • Build scoring rubrics and evaluation methodologies for: • Statistical reasoning • Predictive modeling • Machine learning workflows • Data interpretation • Experimental design • Business analytics and decision-making • Ensure consistent quality standards across datasets and evaluation processes. • Work closely with AI researchers, engineers, and domain experts to maintain accuracy, consistency, and rigor in training data development.

• Translate business briefs in English into clear technical plans, execution steps, and deliverables. • Work independently during the first 2–3 months, managing active data projects from raw data review to final dashboard delivery. • Design analytical data architectures, including dimensional models, star schemas, table relationships, and transformation strategies. • Build real-time or near real-time Power BI dashboards and reporting solutions for executive decision-making. • Develop advanced DAX measures and structure scalable Power BI semantic models. • Define, calculate, and validate KPI logic such as cost of goods, waste, loss index, profitability by product/client/period, cash flow, and operational KPIs. • Use Microsoft 365 tools to automate data intake, processing, and reporting workflows. • Assess which tools, licenses, and technical resources are required for each project and advise on the most efficient setup. • Review unstructured raw data files, identify inconsistencies, document gaps, and ask the right questions before moving forward. • Validate all figures before delivery, ensuring zero calculation errors in client-facing dashboards and reports. • Document assumptions, sources, formulas, data logic, workflows, and technical decisions for long-term maintainability. • Establish working standards such as file structure, naming conventions, Git workflow, and reusable project kickoff templates. • Support the future growth of the data team by helping onboard, delegate to, and supervise junior/intermediate profiles and additional support roles. • Provide technical mentoring, feedback, and quality control once the team expands.

• Responsible for all activities of clinical trial data management including CRF design, database and edit check development • Plan and implement the data management portions of clinical research projects • Develop systems for organizing data to analyze, identify and report trends • Work with clinical leads, CRAs and statistical department to provide accurate data acquisition • Responsible for timely identification of problems or issues that could affect the results or timely completion of the trial • Provide data management portions of clinical study reports, and participates in report review

• Менеджмент та розвиток команди продуктової й маркетингової аналітики — онбординг, менторинг, постановка цілей, оцінка результатів та побудова ефективних процесів роботи. • Робота з ключовими фінансовими показниками: прогнозування Revenue, LTV, ROI-аналіз маркетингових кампаній. • Розвиток аналітичної інфраструктури: дашборди, звіти та автоматизації, а також трансформація інсайтів у конкретні бізнес-рішення. • Впровадження єдиних стандартів метрик, термінології та аналітичних підходів у всіх продуктах Lift • Взаємодія з CEO, CPO та Head of Marketing — допомога командам у знаходженні точок зростання.