Senior Machine Learning Engineer

About the Role

Key Responsibilities

• Develop data flow pipelines to extract, transform, and load data from various data sources into enterprise data lake and data warehouse systems in three regions in AWS.
• Provide data analytics and predictive analysis to business users.
• Assist in the design and development of data pipelines for the Global Data and Analytics team, including data cleaning and transformation.
• Explore and understand various datasets used in biotech/pharma commercial data analytics and create informative data visualizations.
• Work with Data Scientists to perform data cleaning, statistical analysis, feature engineering, and develop pipelines for model selection, training, and evaluation.
• Understand experimental design and conduct A/B tests for data-driven decision-making.
• Ensure consistent feature engineering between training and model serving.
• Automate model deployment, monitoring, and retrain processes.
• Adhere to best practices for coding, testing, and designing reusable code/components.
• Work flexibly on data engineering or machine learning projects based on current product backlog.
• Explore new tools and technologies to improve ETL platform performance and machine learning operations.
• Collaborate effectively with cross-functional teams including data engineers, analysts, and business stakeholders.
• Convey insights and findings to non-technical stakeholders through strong communication skills.
• Stay updated with the latest trends and advancements in data science and machine learning technologies.
• Mentor junior data/machine learning engineers.

Requirements

• Doctorate degree or
• Master's degree and 2 years of Data Science/Machine Learning experience or
• Bachelor's degree and 4 years of Data Science/Machine Learning experience or
• Associate's degree and 8 years of Data Science/Machine Learning experience or
• High school diploma / GED and 10 years of Data Science/Machine Learning experience.

Nice to Have

• Strong programming skills in Python or R, with libraries and packages related to data manipulation, statistical analysis, chart/plot, and machine learning algorithms and frameworks.
• Outstanding analytical and problem-solving skills; ability to learn quickly; experience in model selection, training, and evaluation.
• Familiarity with PySpark dataframe and data processing libraries, machine learning frameworks (like TensorFlow, Keras, or PyTorch), and other ML libraries.
• Experience with Machine Learning lifecycle tools such as feature store, MLflow, model registry, deployment, and monitoring.
• Proficiency in statistical techniques and hypothesis testing, including regression analysis, clustering, and classification.
• Experience with data modeling for OLAP and OLTP databases, and hands-on SQL experience, especially SparkSQL performance tuning.
• Experience with software DevOps CI/CD tools, GitLab.
• Familiarity with cloud platforms such as AWS, Azure, or Google Cloud.
• Knowledge of NLP techniques for text and sentiment analysis.
• Experience analyzing time-series data for forecasting and trend analysis.
• Experience with Docker containers, Kubernetes orchestration.
• Experience with Databricks, Apache Airflow, and Apache Spark, including Spark performance tuning.
• Experience in the Pharmaceutical industry and commercial operations.

Qualifications

• Formal educational background as specified in requirements (Doctorate, Master's, Bachelor's, Associate's, or High School diploma/GED).

Benefits & Perks

• Comprehensive employee benefits package including a Retirement and Savings Plan with generous company contributions.
• Group medical, dental, and vision coverage.
• Life and disability insurance.
• Flexible spending accounts.
• Discretionary annual bonus or sales-based incentive plan.
• Stock-based long-term incentives.
• Award-winning time-off plans.
• Flexible work models, including remote and hybrid arrangements where possible.