Data Analysis Engineer

Role Overview

Data Analysis Engineer

Key Responsibilities

• Architect and develop a robust, scalable recommendation system using structured and unstructured data.
• Design data pipelines and models to collect, process, and analyse behavioural, demographic, and physiological inputs.
• Apply machine learning and statistical techniques to predict user preferences and outcomes.
• Work closely with data engineers, product managers, and medical advisors to refine personalisation logic.
• Continuously evaluate and tune algorithms for accuracy, diversity, and user satisfaction.
• Design and monitor performance metrics (CTR, precision/recall, RMSE, etc.) to assess and improve model quality.
• Translate business questions into data queries and actionable insights.
• Document model assumptions, architecture, and validation rigorously to meet audit and compliance standards.

Required Qualifications

• Bachelor’s or Master’s in Computer Science, Statistics, Data Science, or related field.
• 3–6 years of hands-on experience building data products, ideally involving recommendation engines or personalisation systems.
• Strong programming skills in Python or Scala, with proficiency in libraries like Pandas, NumPy, Scikit-learn, PyTorch, or TensorFlow.
• Experience with SQL and NoSQL databases (e.g., PostgreSQL, MongoDB).
• Proven ability to build and deploy models using ML frameworks and MLOps tools.
• Deep understanding of algorithms such as collaborative filtering, matrix factorization, embeddings, and content-based filtering.
• Exposure to real-time data ingestion systems like Kafka, Spark Streaming, or Flink is a plus.

Preferred Skills

• Prior experience in healthtech, wellness, or behavioral analytics domains.
• Knowledge of A/B testing, causal inference, or reinforcement learning.
• Experience with AWS, GCP, or Azure cloud platforms.
• Comfort working with multi-disciplinary teams and translating complex data into user-friendly insights.
  

Why Join Us?

• Be part of a mission-driven company transforming preventive healthcare through intelligent personalization.
• Work with real-world data to improve lives, not just metrics.