Senior Data Scientist (Recommendation Systems & GenAI)

Join Whizdom.ai as a Senior Data Scientist and take ownership of the models powering our recommendation systems, personalisation, and GenAI-driven features. You'll collaborate closely with our ML and Platform teams to turn data and product goals into impactful solutions that deliver measurable value to our customers.

About the company:

Whizdom AI is an early-stage AI startup building products around recommendation systems, personalisation, and GenAI agents. We are a small team working directly on real customer problems, shipping quickly, measuring outcomes, and iterating fast. Everyone here is expected to take ownership, improve systems proactively, and help build the engineering foundations the company will scale on.

The Role:

We are hiring a Senior Data Scientist to develop and own the models behind our recommendation systems, personalisation, and GenAI-powered features. You will work closely with our ML and platform teams to turn data and product goals into shipped models that create measurable customer value. In a team of our size, this is a hands-on, end-to-end role: you may touch data analysis, feature engineering, model development, evaluation, and production iteration in the same week. We care about people who can move from data and hypotheses to shipped systems and business impact, not research for its own sake.

What You’ll Own:

• Design, build, and evaluate recommendation, personalisation, and ranking models that power core product experiences;
• Develop and apply NLP and GenAI techniques, including retrieval-augmented generation (RAG) and prompt-based approaches, that support recommendations and agent workflows;
• Perform data analysis and feature engineering on large, complex, and often messy datasets to surface signal and improve model inputs;
• Build and maintain offline and online evaluation frameworks to measure model quality, relevance, latency, and business impact;
• Investigate failure modes and run error analysis to make practical tradeoffs between quality, speed, and complexity;
• Partner with engineering and platform teams to deploy, monitor, and iterate on models in production;
• Communicate findings and recommendations clearly to both technical and non-technical stakeholders;
• Stay current with advances in recommendation systems, NLP, and GenAI, and bring relevant techniques into our roadmap;
• Maintain clear documentation of models, pipelines, and methodologies, and ensure data handling complies with privacy and security regulations (e.g., GDPR, CCPA).

What Success Looks Like in the First 6 Months:

• You ship a measurable improvement to a recommendation, personalisation, or GenAI-powered feature in production;
• You establish a practical evaluation framework for at least one core model or agent workflow;
• You turn an ambiguous data or product question into a clear analysis, experiment, and recommendation;
• You improve at least one metric that matters: relevance, engagement, conversion, retention, latency, or cost efficiency;
• You become a trusted voice on how Whizdom AI builds and evaluates its models.

What We’re Looking For:

• 4+ years of experience in data science or machine learning, with a strong focus on recommendation systems, personalisation, or ranking;
• Hands-on experience training, evaluating, and deploying models in production, including NLP or GenAI-based approaches;
• Proficiency in Python, with solid experience in ML frameworks (PyTorch, TensorFlow, or scikit-learn) and data libraries (Pandas, NumPy);
• Strong understanding of recommendation techniques such as collaborative filtering, content-based filtering, and ranking;
• Working knowledge of GenAI/LLM concepts: prompting, retrieval-augmented generation (RAG), fine-tuning, or evaluation of GenAI systems;
• Experience with cloud platforms (AWS, GCP, or Azure) and modern data infrastructure (e.g., Spark, Snowflake, BigQuery, or Databricks);
• Familiarity with MLOps practices: version control, containerization (Docker, Kubernetes), and experiment tracking (e.g., MLflow, Weights & Biases);
• Strong quantitative and analytical skills, with the ability to communicate technical work to non-technical stakeholders.

Nice to Have:

• Experience with reinforcement learning, multi-armed bandits, or other decision-optimisation techniques;
• Experience with vector databases, retrieval infrastructure, or agent orchestration frameworks (e.g., LangChain, LlamaIndex);
• Experience running A/B tests or online experiments;
• Prior experience in an early-stage startup or fast-growing company;
• Contributions to the AI/ML community through publications, talks, or open-source projects.

A Note on Fit:

You do not need to match every bullet to apply. We care more about learning velocity, technical judgment, and evidence that you can ship hard things than about perfect keyword coverage or pedigree.

Why Join:

• 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, not just one or the other;
• Flexible remote environment with strong overlap with European time zones preferred;
• Small team, low bureaucracy, and a lot of room to grow. Competitive salary and equity package.

If you find this opportunity right for you, don't hesitate to apply or get in touch with us if you have any questions!