Lead Data Scientist
New
Anywhere in Europe and South AmericaFull-TimeLead
Salary not disclosed
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Job Details
- Languages
- English
- Experience
- 5+ years
- Required Skills
- PythonSQLMachine LearningPyTorchData sciencePandasscikit-learn
Requirements
- 5+ years of professional experience in Data Science, Machine Learning, or Applied ML roles.
- Demonstrated experience operating as the sole or lead Data Scientist on a product or team.
- Strong experience with supervised and unsupervised ML, modern ML/data tooling.
- Practical familiarity with representation learning, sequence modeling, Transformers, LLMs, or GenAI systems.
- Experience handling large-scale structured, unstructured, event, or interaction datasets.
- Advanced proficiency in Python and SQL.
- Hands-on experience using tools such as PyTorch, scikit-learn, pandas/Polars, experiment tracking, and production ML workflows.
- Experience deploying ML models, data pipelines, or intelligent systems into production.
- Familiarity with Task Mining, Process Mining, event-log analysis, behavioral analytics, workflow automation.
Responsibilities
- Act as the founding Data Scientist on the product: define the DS strategy, choose the right tools and frameworks, and establish best practices.
- Design and build Task Mining and Process Mining solutions that transform raw interaction data into discovered workflows, patterns, bottlenecks, and optimization opportunities.
- Design, develop, and deploy ML systems and data pipelines for large-scale structured, unstructured, and event/interaction data.
- Build predictive and pattern-discovery solutions using supervised and unsupervised learning, representation learning, sequence modeling, and LLM/GenAI approaches where appropriate.
- Establish practical foundations for dataset construction, labeling strategy, offline/online evaluation, monitoring, feedback loops, and human-in-the-loop review where needed.
- Own projects end-to-end, from problem framing and experimentation through production deployment and iteration.
- Collaborate closely with engineering on data instrumentation, pipeline design, deployment, and integration of production-ready services.
- Communicate findings, tradeoffs, and technical concepts effectively to both technical and business stakeholders.
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