> Markdown version of [/jobs/ext/4695064-senior-data-scientist-recommendation-systems-genai](https://www.wearedevelopers.com/jobs/ext/4695064-senior-data-scientist-recommendation-systems-genai). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md).

---

# Senior Data Scientist (Recommendation Systems & GenAI)

- **Company:** WHIZDOM, LLC
- **Location:** United States (Remote available)
- **Experience:** Expert
- **Contract:** Permanent contract
- **Skills:** A/B Testing, Artificial Intelligence, Amazon Web Services, Data Analysis, Microsoft Azure, BigQuery, Data Infrastructure, Python (Programming Language), Machine Learning, NumPy, Open Source Technology, Performance Tuning, Recommender Systems, Tensorflow, Reinforcement Learning, Data Processing, Feature Engineering, Pytorch, Large Language Models, Snowflake, Multi-Agent Systems, Apache Spark, Llamaindex, Generative AI, Pandas, Containerization, Scikit Learn, Kubernetes, Machine Learning Operations, Software Version Control, Docker, Databricks
- **Published:** November 12, 2026
- **Apply:** https://arc.dev/remote-jobs/j/redirect/pyvvcmg1d4

## About the Role

* 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.

## Description

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., 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.