• Lead forecasting initiatives by designing and implementing advanced time series models to predict sales and behavioural trends for thousands of customers. • Develop scalable machine learning solutions that power real-time decision-making across risk management, fraud detection, and product personalization. • Collaborate cross-functionally with product managers, engineers, and business stakeholders to translate complex data challenges into actionable insights and measurable business outcomes. • Drive innovation in fintech applications by experimenting with cutting-edge approaches (e.g., deep learning architectures, probabilistic forecasting, and transformer-based models). • Ensure compliance and transparency in model development, aligning with industry regulations and ethical standards for financial data usage. • Shape the data science strategy by identifying opportunities where predictive modeling can unlock new value streams and competitive advantages.

• Own and evolve the product vision, strategy, and roadmap for MarketSmart • Define clear priorities and success metrics to guide roadmap decisions • Lead ongoing product discovery to understand customer workflows and pain points • Translate customer, market, and performance insights into product requirements • Collaborate with Engineering and Product Design to define and deliver solutions • Analyze product usage data, campaign performance metrics, and customer feedback • Lead validation efforts including beta programs, usability testing, and post-launch feedback

• 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.