Data Scientist
Noida, Uttar PradeshFull-TimeMid-levelAI / Data Science
Key Responsibilities
- Data Analysis & Research: Analyze large datasets using queries and scripts to extract meaningful insights and identify opportunities for improving complex ML and bidding systems.
- Simulation & Modelling: Design and execute simulations to validate hypotheses, quantify efficiency gains, and model system performance.
- Experimentation & Causal Inference: Develop robust experiment designs and metric frameworks to deliver unbiased, data-backed insights for product and business decisions.
- End-to-End ML Deployment: Build, train, and deploy ML models into production environments, managing the full lifecycle including versioning, monitoring, and retraining.
- Scalability & Performance Optimization: Operationalize ML models at scale, optimizing for performance, reliability, and cost efficiency in real-world production systems.
- Cross-Functional Collaboration: Work closely with product, engineering, and data teams to translate business problems into analytical solutions.
Key Qualifications
- Master’s degree in a quantitative field (e.g., Computer Science, Statistics, Mathematics, Data Science) or equivalent experience.
- 3+ years of professional experience in data science or applied machine learning.
- Strong problem-solving and analytical skills, with the ability to turn complex product questions into actionable insights.
- Excellent communication skills, both verbal and written, with the ability to present technical results to non-technical audiences.
- Proven ability to build and maintain strong relationships with stakeholders across teams and functions.
- Deep understanding of machine learning algorithms, from classical methods (e.g., regression, random forests, k-means) to advanced techniques (e.g., gradient boosting frameworks such as XGBoost, LightGBM, CatBoost, and transformer-based architectures like BERT or Sentence Transformers).
- Proficiency in Python or R, and data manipulation tools/libraries such as Pandas and SQL.
- Hands-on experience deploying models in production and managing ML lifecycle processes (monitoring, retraining, version control).
Preferred Skills
- Experience with cloud platforms (GCP, AWS, or Azure).
- Familiarity with MLOps frameworks for deployment, monitoring, and automation.
- Exposure to big data tools (e.g., Spark, BigQuery).
- Understanding of A/B testing, experimentation frameworks, and causal inference techniques.
