Our client, a world leader in biotechnology and life sciences, is looking for a “Senior Data Scientist”.

Location: South San Francisco, CA
Job Duration: Long-Term Contract (Possibility Of Extension)
Company Benefits: Medical, Paid Sick Leave, 401 (k)

Seeking a Senior Data Scientist to develop, deploy, and industrialize machine learning solutions using structured and unstructured data across Commercial and Medical domains. This role will partner with business and technical teams to translate complex challenges into impactful, data-driven solutions while ensuring strong data quality, governance, model validation, and regulatory compliance.

Required Skills & Qualifications

Bachelor's degree in Statistics, Mathematics, Computer Science, or a related quantitative field.
5+ years of experience in Data Science or related roles.
Strong proficiency in Python and/or R.
Strong SQL skills.
Expertise in Machine Learning and Deep Learning techniques, including classification, regression, and forecasting.
Experience developing end-to-end ML solutions, from concept and prototyping through production deployment and monitoring.
Experience with cloud platforms such as AWS or GCP.
Strong understanding of MLOps, including CI/CD, model versioning, performance monitoring, scalability, and reliability.
Experience collaborating with Data Science Product Owners, Data Leads, ML Engineers, and MLOps teams.
Commitment to data ethics, governance, model validation, and regulatory compliance.

Preferred Skills

Healthcare or pharmaceutical industry experience.
Experience with TensorFlow or PyTorch.
Hadoop, Spark, or other big-data platforms.
Predictive modeling, text classification, clustering, ROI analysis, and insight generation.
Data visualization tools such as Tableau, Qlik, or Data Studio.
Publications, open-source contributions, or relevant Data Science/ML/AI certifications.

If interested, please share your updated resume at hr@dawarconsulting.com/saurav@dawarconsulting.com.

Gerekli beceriler

PythonRSQLMachine Learning and Deep Learning techniques (classification, regression, forecasting)End-to-end ML solution development (prototyping, production deployment, monitoring)Cloud platforms (AWS or GCP)MLOps (CI/CD, model versioning, performance monitoring, scalability, reliability)Cross-functional collaboration with Data Science Product Owners, Data Leads, ML Engineers, MLOps teamsProblem framing / translating ambiguous business problems into data-driven analytical solutionsCommunication and presentation to non-technical stakeholdersData ethics, governance, model validation, and regulatory complianceTensorFlow or PyTorchBig-data platforms (Hadoop, Spark)Predictive modeling, text classification, clustering, ROI analysis, and insight generationData visualization tools (Tableau, Qlik, Data Studio)

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