Staff Data Scientist - ML & Recommendations

Location:

About the Company

Alakh Pandey

multi-billion-dollar unicorn

15M+ app downloads

Qualification & Eligibility

• Bachelors or higher degree in a quantitative discipline (Computer Science, Statistics, Engineering, Applied Mathematics).

Work Experience

• Minimum

  6+ years of experience

  .
• Startup experience preferred; Edtech work experience is a bonus.

Roles & Responsibilities

• Model Development:

  Design, train, and deploy recommendation algorithms (collaborative filtering, deep learning, bandits, reinforcement learning, embeddings, etc.).

• Experimentation:

  Set up A/B testing frameworks, design evaluation metrics (CTR, engagement, retention, revenue lift), and analyze causal impact.

• Data & Feature Engineering:

  Build and optimize pipelines to generate high-quality features from large-scale user, content, and interaction data.

• Scalability & Systems:

  Collaborate with engineering to ensure models are production-ready, scalable, and low-latency.

• Domain Leadership:

  Drive innovation in personalization strategies (multi-objective optimization, cold-start solutions, contextual recommendations, etc.).

• Mentorship:

  Provide technical leadership, code reviews, and best practices to other scientists.

Skill Sets Required

• Core ML:

  Strong background in machine learning fundamentals supervised/unsupervised learning, regression/classification, probability & statistics.

• Recommendation Systems:

  Deep understanding of collaborative filtering, matrix factorization, sequence models, embeddings, two-tower retrieval models, deep retrieval/ranking architectures, and multi-task learning.

• Programming:

  Proficiency in Python (NumPy, pandas, scikit-learn, PyTorch/TensorFlow, JAX). Strong SQL skills.

• Experimentation:

  Expertise in A/B testing design, statistical significance, confidence intervals, and bias correction.

• Systems/Infra Awareness:

  Familiarity with model serving, feature stores, online/offline training pipelines, and latency optimization.

• Communication:

  Ability to translate technical insights into business impact and influence product/engineering roadmaps.

Good to Have:

• Experience with big data tools (Spark, Trino, Presto, Hadoop, BigQuery, Redshift).
• Leveraging LLMs for personalization.