• Creating new and improving existing AI solutions • Involvement in the whole process of data-driven development from analyzing product requirements to launching the feature or system to production (with the help of MLOps specialists) and improving it based on results of monitoring and feedback • Communicating with fellow data scientists, product owners, and developers • Applying best software engineering practices to build a reliable codebase • Performing code reviews • Working in agile environment and presenting the results of your work on regular sprint reviews

Role Description You will build and validate the models that define, expand, and score the audiences advertisers activate across TV, CTV, digital, social, and programmatic. Working across survey, purchase, and cross-platform media data, you turn messy multi-source signals into accurate, explainable, privacy-safe segments, and you help shape the product itself with ideas for new audience capabilities. You will partner closely with ML Engineers and the US product and analytics team. What you'll do - Build statistical and ML models to create, expand, and score audience segments from survey, panel, purchase, and media-exposure data. - Develop propensity and lookalike models that scale small seed audiences to addressable populations. - Lead data fusion work, combining deterministic and probabilistic sources into one representative consumer view while correcting for bias. - Ideate on making the product better: propose new audiences, features, and methods, and prioritize them with the US team. - Own audience measurement analytics: reach, overlap, index strength, and incremental lift (A/B, matched control, causal inference). - Partner with ML Engineering to move models into reproducible, monitored production, under privacy-by-design principles. Qualifications - ~5+ years in applied data science, with models that reached production or client delivery. - Sound statistical modeling (survey stats, causal inference, experimental design, propensity/uplift). - Working knowledge of Databricks; comfortable in a Spark / Snowflake environment. - Strong Python (pandas, scikit-learn, statsmodels) and SQL against large databases. - Degree in a quantitative field (Statistics, Data Science, Economics, CS, Math, or similar). Requirements - Media / advertising background (strong plus). - Audience/identity or ad tech exposure (DSP/SSP, DMP/CDP, clean rooms, identity graphs). - Familiarity with privacy-preserving methods and GDPR/CCPA in data collaboration. Company Description Fusemachines is a leading AI strategy, talent, and education services provider. Founded by Sameer Maskey Ph.D., Adjunct Associate Professor at Columbia University, Fusemachines has a core mission of democratizing AI. With a presence in 4 countries (Nepal, the United States, Canada, and the Dominican Republic) and more than 450 full-time employees, Fusemachines brings global AI expertise to transform companies worldwide. Founded in 2013, Fusemachines is a global provider of enterprise AI products and services, on a mission to democratize AI. Leveraging proprietary AI Studio and AI Engines, the company helps drive the clients’ AI Enterprise Transformation, regardless of where they are in their Digital AI journeys. With offices in North America, Asia, and Latin America, Fusemachines provides a suite of enterprise AI offerings and specialty services that allow organizations of any size to implement and scale AI. Fusemachines serves companies in industries such as retail, manufacturing, and government. Fusemachines continues to actively pursue the mission of democratizing AI for the masses by providing high-quality AI education in underserved communities and helping organizations achieve their full potential with AI.

• Support the successful execution of power generation projects from early development through construction, commissioning, and commercial operation. • Develop and maintain project plans, schedules, budgets, resource forecasts, and execution strategies. • Coordinate day-to-day activities across EPC contractors, engineering consultants, equipment suppliers, and internal stakeholders. • Track project risks, issues, milestones, and action items to help ensure safe, on-time, and on-budget delivery. • Manage first-of-a-kind (FOAK) technology pilot projects that validate new power generation and control system solutions before commercial deployment. • Coordinate the integration of complex hardware and software systems, including natural gas engines, Battery Energy Storage Systems (BESS), medium-voltage switchgear, load banks, and grid and PV simulators. • Support engineering activities from FEED (FEL 1–3) through Issued for Construction (IFC) documentation. • Monitor project budgets, purchase orders, invoices, and financial performance. • Administer construction documentation, including RFIs, submittals, technical deliverables, dashboards, and project status reports for leadership.

• Lead the design, development, and deployment of scalable machine learning solutions focused on pricing optimization, demand forecasting, and promotion planning. • Build and improve statistical and machine learning models for: - Demand forecasting - Price elasticity modeling - Promotion optimization - Inventory and revenue forecasting • Design, build and deploy robust ML pipelines in Databricks, build model monitoring systems, and production-ready APIs. • Ability to design and evaluate transformer-based time series forecasting models for large-scale retail sales forecasting and demand planning. • Drive experimentation, model evaluation, and continuous improvement of forecasting and pricing models. • Analyze large-scale structured and unstructured retail datasets to uncover trends, customer behavior patterns, and pricing insights. • Develop data-driven strategies that help maximize revenue, profitability, and pricing efficiency across products and categories. • Apply advanced statistical techniques and machine learning algorithms to solve complex retail business problems. • Collaborate closely with Product, Engineering, Data Engineering, and Business teams to translate business requirements into scalable ML solutions. • Mentor junior data scientists and provide technical guidance across cross-functional teams. • Communicate analytical findings and business recommendations clearly to both technical and non-technical stakeholders.