Senior Data Scientist, Zenjob

• 4+ years of applied Data Science experience, ideally in marketplaces or other matching-heavy environments
• A curious, proactive, impact-driven mindset with strong business acumen
• Enjoy working in environments where things move fast, priorities evolve, and iteration happens quickly
• Solid ML expertise in scoring, ranking, and optimization techniques used in matching and allocation systems
• Experience building forecasting models using time-series and probabilistic approaches
• Strong experimentation skills, including A/B testing and quasi-experimental methods, with the ability to interpret results clearly
• Proficiency in SQL and Python with the ability to move from exploration to production
• Clear communication and strong product thinking, able to influence decisions and define success metrics

Desirable

• At Zenjob, you’ll work on data products that actually ship and shape how thousands of people find work every day.
• Your job is to understand how our marketplace behaves, build and deploy models that improve it, and work closely with product and engineering to make sure those models deliver real impact instead of staying in a notebook
• Shape product decisions: Data should guide decisions from the start. You will proactively explore our data to identify product opportunities, inefficiencies, and growth levers. You’ll translate complex findings into actionable insights that help PMs and business leaders prioritize what truly matters
• Forecast marketplace dynamics: Strong forecasting prevents shortages and overbooking. You will model supply and demand patterns using robust time-series and probabilistic methods that support planning and operational decisions
• Improve matching efficiency: Better matching means higher fill rates, happier customers, and less manual work. You will build and refine ranking and allocation models so the right talent reaches the right job quickly and reliably, whether through ML or other data-driven methods
• Experiment with confidence: Good experiments beat guesses. You will design and run end-to-end experiments using methods suited for a two-sided marketplace (A/B, quasi-experimental approaches, switchbacks, holdouts) and interpret results with clarity
• Ship models that matter: Models only matter when users benefit from them. You will own the process of turning analyses or models into production, working with engineering on monitoring, fallback strategies, and safe rollouts