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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.
Job Responsibility:
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
Requirements:
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
Nice to have:
Experience with dbt or data modeling
Understanding of data pipeline design and collaboration in environments with limited DE support
What we offer:
28 days of paid vacation (increased by 1 day for every 2 calendar years of service with us, up to a maximum of 30 days)
1 day of special leave for community service
5 days of paid educational leave
Possibility of unpaid leave
Hybrid working model
Dog-friendly office
Annual training budget of EUR 750
Mentoring opportunities
Constant career development conversations
30 EUR Urban Sports Club grant per month
EUR 40 credit per month for NAVIT (sustainable mobility tool)
Quarterly team events
Winter and summer parties
Budget for birthdays and anniversaries of our employees
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