Data Scientist - Pharma(CRO,CDMO)

• 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 45 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:

• ? 45 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 (Bachelors minimum; Masters 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