Data Scientist

Role : Junior Data Scientist Experience : Minimum 4 years Location : Hyderabad About the role: We are scaling an AI innovation team focused on practical, high-impact use cases for pharma manufacturers and commercial operations (CMO, CDMO, CRM). In this role you will rapidly prototype ML/AI solutions, work with cross-functional stakeholders (R&D, Process Development, Quality, Manufacturing, Commercial), and contribute to moving validated models toward production. This role is ideal for a pragmatic scientist/engineer with 4–5 years of applied data science experience who wants to focus on pharma problems (process analytics, PAT, sensor data, imaging, customer analytics) and learn regulated deployment practices. Responsibilities: ➢ Collaborate with domain teams to translate business/regulatory problems into data science hypotheses and testable experiments. ➢ Acquire, clean, and integrate manufacturing (MES, LIMS, PAT, sensor/time-series, batch records), laboratory, and CRM/commercial datasets. ➢ Rapidly prototype models and algorithms: regression, tree ensembles, time-series forecasting, anomaly detection, clustering/segmentation, and basic deep learning (e.g., CNNs for imaging, RNNs/Temporal models). ➢ Build explainability and uncertainty estimates into prototype models for regulated decision-support. ➢ Validate model performance using cross-validation, holdout sets, and domain-appropriate metrics; participate in documentation needed for audits/validation. ➢ Implement monitoring experiments (drift detection, simple retraining pipelines) and hand off monitoring requirements. ➢ Contribute to reproducible code, notebooks, and lightweight technical documentation; follow data governance and security policies. ➢ Present findings to technical and non-technical stakeholders; translate model results into actionable recommendations. Required qualifications: ➢ 4–5 years of hands-on experience in applied data science, machine learning, or analytics (industry or research with applied projects). ➢ Degree in Data Science, Computer Science, Statistics, Engineering, Biostatistics, Chemistry, or related quantitative field (Bachelor’s minimum; Master’s preferred). ➢ Strong Python skills (pandas, scikit-learn, numpy); working knowledge of SQL. ➢ Practical experience with time-series/sensor data or tabular modeling in production-like settings. ➢ Experience with at least one deep learning framework (PyTorch or TensorFlow) for applied tasks. ➢ Demonstrated ability to move from problem definition to prototype and present results to stakeholders. ➢ Good documentation practices, basic testing, and reproducible analysis (notebooks + script refactors). ➢ Clear communication skills and ability to work in cross-functional teams. ➢ Willingness to work in regulated environments and follow documentation/validation processes. Preferred qualifications: ➢ Prior experience in pharma, biotech, CMO/CDMO, manufacturing, or other regulated industries. ➢ Familiarity with MES, LIMS, ELN, PAT, or industrial IoT data sources. ➢ Experience with MLOps basics (Docker, simple CI/CD, MLflow, Airflow) or production handoffs. ➢ Knowledge of multivariate statistical process control (MSPC), DOE, chemometrics, or Six Sigma concepts. ➢ Exposure to LLMs and prompt engineering for knowledge extraction, summarization or augmentation of domain content (SOPs, batch records). ➢ Experience with cloud platforms (AWS/Azure/GCP) and data platforms (Snowflake, Redshift, BigQuery). ➢ Understanding of model explainability (SHAP, LIME) and uncertainty quantification techniques. Technical stack: ➢ Languages: Python (pandas, scikit-learn, xgboost/lightgbm), SQL ➢ DL: PyTorch or TensorFlow/Keras ➢ Time-series: tsfresh, statsmodels, prophet, tslearn ➢ MLOps / infra: Docker, MLflow, Airflow (familiarity) ➢ Storage / Viz: S3 / object store, Postgres, Tableau / Power BI / Plotly ➢ Tools: Git, Jupyter / VS Code, basic Linux shell