Senior Data Scientist, Recommendation Systems

A fast‑growing digital product organization is seeking a senior data scientist to serve as a cross‑team subject matter expert for insurance plan recommendation engines. This role operates as a senior individual contributor with broad influence across multiple delivery squads, shaping modeling strategy, elevating data science standards, and guiding the evolution of advanced personalization and recommendation systems.

The position is hands‑on and product‑focused, with a strong emphasis on algorithms, delivery quality, and mentorship. The role is designed for a senior practitioner who thrives in ambiguous problem spaces, influences without formal authority, and bridges business needs with advanced data science solutions.

Responsibilities

• Act as a data science subject matter expert across multiple product squads supporting recommendation engine initiatives.
• Influence modeling approaches, technical decisions, and delivery standards across teams without direct managerial authority.
• Elevate data science practices, including problem framing, backlog quality, modeling rigor, and delivery execution.
• Mentor and support data science leads through coaching, technical guidance, and best‑practice sharing.
• Drive the evolution of recommendation systems toward more advanced ranking, personalization, and user behavior models.
• Help define future‑state modeling strategies and architectural direction for recommendation and personalization platforms.
• Partner with product and engineering stakeholders to translate user needs and business objectives into scalable data science solutions.
• Contribute directly to high‑impact modeling work while maintaining a strong focus on production readiness and reliability.
• Operate effectively in environments with high autonomy, shifting priorities, and incomplete problem definitions.

Required experience and skills

Data science and modeling expertise

• Deep expertise in data science, machine learning, and advanced analytics.
• Strong proficiency in Python and SQL.
• Proven ability to frame complex product and business problems into actionable modeling approaches.
• Experience building and deploying models in production environments.

Recommendation and personalization systems

• Hands‑on experience designing or implementing recommendation engines.
• Experience with ranking systems, personalization models, or user behavior modeling.
• Demonstrated success delivering recommendation or personalization systems that support product growth.

System and platform experience

• Experience contributing to scalable data science systems or platforms.
• Exposure to experimentation frameworks and model evaluation approaches.
• Ability to collaborate on architectural decisions that support long‑term scalability and reuse.

Leadership and collaboration

• Strong mentorship skills with the ability to lead by example.
• Proven ability to influence teams and technical direction without formal authority.
• Strategic mindset with the ability to connect user needs to technical and algorithmic solutions.
• Comfort operating with ambiguity, autonomy, and evolving requirements.
• Strong communication skills across technical and non‑technical stakeholders.

Ideal background

• Senior‑level data scientist with a strong delivery track record.
• Experience building recommendation systems in production.
• Product‑oriented mindset with a focus on algorithms, modeling quality, and measurable outcomes.

FAQ

1. What are the core responsibilities of a Senior Data Scientist in recommendation systems?
This role focuses on designing, building, and optimizing recommendation engines that personalize user experiences. It includes developing algorithms that suggest products, content, or services based on user behavior and preferences. The role also involves deploying models into production and continuously improving their performance.

2. What types of recommendation techniques are used in this role?
Common techniques include collaborative filtering, content-based filtering, and hybrid approaches. Advanced methods may involve deep learning, matrix factorization, and reinforcement learning. The choice of technique depends on data availability and business goals.

3. What data is typically used to build recommendation systems?
Data sources include user interactions, transaction history, browsing behavior, and contextual information. Additional data such as product attributes or content metadata may also be used. High-quality and well-structured data is essential for accurate recommendations.

4. What tools and technologies are commonly used in this role?
Tools include Python, SQL, and machine learning frameworks such as TensorFlow or PyTorch. Big data technologies like Spark and data pipelines are often used for large-scale processing. Experimentation and A/B testing platforms are also important.

5. How is model performance evaluated in recommendation systems?
Performance is evaluated using metrics such as precision, recall, mean average precision (MAP), and normalized discounted cumulative gain (NDCG). Online metrics like click-through rate (CTR) and conversion rate are also critical. Continuous experimentation helps refine models.

6. How are recommendation systems deployed and scaled?
Models are deployed using scalable infrastructure, often on cloud platforms. Real-time and batch processing systems are used depending on use cases. Monitoring and optimization ensure consistent performance at scale.

7. What challenges are common in building recommendation systems?
Challenges include handling sparse data, cold-start problems, and balancing personalization with diversity. Ensuring scalability and low latency is also critical. Maintaining fairness and avoiding bias can be complex.