Machine Learning Data Scientist (CI/CD)

Job Description

Client: Large Biopharma

Job ID: 9908

Pay Rate: $75/hr. W2

Contract: 1 Year (Renewable)

Location: Remote (EST or CST timezone)

Openings: 2

Required:

• Experience with CI/CD, orchestration for AI/ML (Machine Learning) pipeline development
• Python (required)
• Minimum 2-4 years of experience depending on education level (refer to JD)
• Experience is not required in pharma/biotech; candidates from other industries like Google are considered.

Nice to have / Skills:

• Proficiency in R or Scala
• Experience in life sciences or healthcare
• Experience in global, complex organizations
• Exposure to tools like DataRobot, Dataiku, Alteryx

Experience Level:

• PhD with 2+ years analytical experience OR
• Master’s with 4+ years analytical experience

Commitment: Full-time only, no conflicting employment

Key Responsibilities & Requirements:

• Hands-on GenAI/ML modeling with complex datasets; strong theoretical foundation in AI/ML
• Expertise in:
• Supervised/unsupervised learning
• Deep learning, reinforcement learning, federated learning
• Time series analysis, optimization, Generative AI
• Strong Python coding (functions, classes); production-ready software development
• Experience developing and deploying code in agile, AI product-focused teams
• Comfortable working in cloud/HPC environments (AWS, GCP, Databricks, Apache Spark)
• Experience with CI/CD, orchestration for AI/ML pipeline development
• Proficient with:
• Python (required), R (nice to have), Scala (bonus)
• SQL/NoSQL, Snowflake, cloud databases
• Excellent communication, data visualization, and storytelling skills
• Ability to simplify technical concepts for business stakeholders
• Team-oriented with ability to manage multiple priorities in fast-paced environments
• Familiarity with Tableau, Power BI, Plotly, Git/version control, APIs

Note: The repeated 'Required' and 'Nice to have' sections have been consolidated to avoid redundancy and improve clarity. The overall formatting has been improved for readability and engagement, using appropriate HTML tags.