Senior Data Scientist
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Job Description
Senior Data Scientist building recommendation, personalization, and GenAI models for Whizdom AI. Owning end-to-end development, evaluation, deployment, and iteration of customer-facing ML systems.
Responsibilities:
- Design, build, and evaluate recommendation, personalization, and ranking models
- Develop and apply NLP and GenAI techniques, including RAG and prompt-based approaches, for recommendations and agent workflows
- Perform data analysis and feature engineering on large, complex, and messy datasets
- Build and maintain offline and online evaluation frameworks for model quality, relevance, latency, and business impact
- Investigate failure modes and conduct error analysis
- Make practical tradeoffs between quality, speed, and complexity
- Partner with engineering and platform teams to deploy, monitor, and iterate on production models
- Communicate findings and recommendations to technical and non-technical stakeholders
- Stay current with recommendation systems, NLP, and GenAI advances and inform the roadmap
- Document models, pipelines, and methodologies
- Ensure data handling complies with privacy and security regulations such as GDPR and CCPA
- Ship measurable improvements to recommendation, personalization, or GenAI-powered features
- Establish evaluation frameworks for core models or agent workflows
- Turn ambiguous data or product questions into analyses, experiments, and recommendations
- Improve metrics such as relevance, engagement, conversion, retention, latency, or cost efficiency
Requirements:
- 4+ years of experience in data science or machine learning, with a strong focus on recommendation systems, personalization, or ranking
- Hands-on experience training, evaluating, and deploying models in production, including NLP or GenAI-based approaches
- Proficiency in Python
- Solid experience with ML frameworks such as PyTorch, TensorFlow, or scikit-learn
- Experience with data libraries such as Pandas and NumPy
- Strong understanding of collaborative filtering, content-based filtering, and ranking
- Working knowledge of prompting, retrieval-augmented generation (RAG), fine-tuning, or GenAI system evaluation
- Experience with AWS, GCP, or Azure
- Experience with modern data infrastructure such as Spark, Snowflake, BigQuery, or Databricks
- Familiarity with version control, Docker, Kubernetes, and experiment tracking tools such as MLflow or Weights & Biases
- Strong quantitative and analytical skills
- Ability to communicate technical work to non-technical stakeholders
- Experience with reinforcement learning, multi-armed bandits, vector databases, retrieval infrastructure, agent orchestration frameworks, A/B testing, early-stage startups, publications, talks, or open-source projects is nice to have
- Strong overlap with European time zones preferred
Benefits:
- Flexible remote environment with strong overlap with European time zones preferred
- Competitive salary and equity package
- Direct access to the founders and real influence over how Whizdom AI builds and evaluates its models
- High-ownership role with room to shape recommendation and GenAI systems used by real customers
- Opportunity to work across both classic ML and modern GenAI problems
- Small team, low bureaucracy, and a lot of room to grow
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