- Technology
Senior Data Scientist
- EF Education First
- Mid-Senior Level
- On-site
- Full-Time
- Budapest, Hungary
Role Summary
The Senior Data Scientist leads end‑to‑end data and ML products across a global organization, owning the predictive models that drive Sales and Marketing efficiency. You will build sophisticated commercial data products that integrate tightly with Salesforce (including Agentforce), Snowflake, and AWS‑native services.
We are looking for a commercially-minded scientist who understands that different business problems require different mathematical approaches, knowing when to build dynamic, fast-moving models versus stable, objective scoring frameworks to optimize Revenue Operations.
Please note: we're trying to build a great company culture and a cohesive office, which means that this is an in-office opportunity. There will be travel - Stockholm, London, Zurich etc. but when in Budapest this is a 5-days in the office role.
Key Responsibilities
Commercial Data Products
- Sales Optimization & Scoring: Design and deploy advanced scoring engines for Leads and Opportunities. You will be responsible for building both dynamic prioritization models (optimizing daily workflows) and robust qualification frameworks (assessing fundamental asset quality).
- Marketing Measurement: Lead the development of Marketing Mix Modeling (MMM) and Multi-Touch Attribution (MTA) to quantify marketing ROI and guide budget allocation across complex channels.
- Bidding & Optimization: Develop algorithmic strategies for Value-Based Bidding (VBB) and customer acquisition, leveraging predictive CLTV and conversion probability.
Machine Learning & Engineering
- End-to-End Pipeline Ownership: Own the full lifecycle from data ingestion to deployment. You will build scalable ELT/ETL pipelines in Snowflake and AWS (S3, Glue, Lambda, Step Functions) to support your models.
- Salesforce Intelligence: Work with Salesforce Data Cloud and Agentforce to operationalize models, ensuring insights are delivered directly into the hands of sales reps and marketers via agents or CRM workflows.
- MLOps & Reliability: Implement robust MLOps practices (CI/CD, drift monitoring, automated retraining) to ensure high availability of scoring services.
Advanced Analytics & Strategy
- Statistical Rigor: Apply advanced techniques including Bayesian inference (crucial for MMM), causal inference, time‑series forecasting, and experimental design to validate business impact.
- Stakeholder Management: Partner with commercial leadership to translate business objectives into technical roadmaps. You will explain complex model behaviors, such as score fluctuations vs. stability, to non-technical audiences.
- Mentorship: Mentor junior team members and define coding standards and best practices for commercial data science.
Requirements
- Education: A Master’s degree in a quantitative field (e.g., Statistics, Computer Science, Econometrics, Mathematics, Physics) is required. A PhD is considered a distinct advantage.
- Experience: 5+ years of hands‑on data science experience in large Global or European‑scale companies as a Senior Data Scientist, with a specific focus on Revenue, Sales, or Marketing operations.
- Commercial Modeling: Proven track record of building models for commercial use cases, such as Lead Scoring, Ranking/Prioritization, Attribution (MTA), or Econometrics (MMM).
- Communication: Excellent ability to frame technical complexity in business terms and influence senior stakeholders.
Plus:
- Tech Stack:
- Snowflake: Expert-level SQL, performance tuning, and data modeling.
- Python: Proficiency in standard ML libraries (scikit‑learn, XGBoost, PyMC, etc.) and production engineering.
- AWS: Experience building data pipelines using AWS-native services.
- Salesforce Ecosystem: Strong familiarity with CRM data structures and integrating ML outputs into Salesforce.
- Statistical Depth: Deep understanding of probability, hypothesis testing, and regression.