Manager, Data Science

at  Trinity Life Sciences

THIS POSITION IS LOCATED ON-SITE AT A CLIENT IN THOUSAND OAKS, CA.

Trinity is seeking a Data Science Manager to join and work with the team of data scientists managing multiple projects. This candidate will work directly with Trinity clients and other Trinity leaders to guide the team in formulation, model development and implementation by liaising with business stakeholders to explain the model outcomes.

ABOUT US

Trinity Life Sciences is a trusted strategic commercialization partner, providing evidence-based solutions for the life sciences. With 25 years of experience, Trinity is committed to revolutionizing the commercial model by providing exceptional levels of service, powerful tools and data-driven insights. Trinity’s range of products and solutions includes industry-leading benchmarking solutions, powered by TGaS Advisors. To learn more about how Trinity is elevating life sciences and driving evidence to action, visit trinitylifesciences.com.
Trinity’s salary bands account for a wide range of factors that are considered in making compensation decisions including but not limited to skill sets and market demand for skills; level of experience and training; specific qualifications, performance, time in role/company, geographic location, and other business and organizational needs. A reasonable estimate of the current range is $132,000-$160,000.
In addition to your base salary, you will also be eligible for an annual discretionary performance bonus.

• Serve as the primary point of contact for client stakeholders to understand their business challenges and modeling needs
• Design, create, and deploy AI/machine learning solutions tailored to our industry-specific data analytics platform.
• Establish scalable, standardized processes for gathering, processing, presenting, and analyzing extensive datasets in a production setting.
• Define problem statements clearly and manage all aspects of data, including acquisition, exploration/visualization, feature engineering, experimentation with machine learning algorithms, and model deployment.
• Develop functional algorithm prototypes, assess and compare accuracy metrics using real-world datasets and communicate results to both technical and non-technical stakeholders
• Provide detailed design specifications, requirements, and guidance to software engineers for algorithm implementation in solution/product development.
• Manage real-time delivery and production pipelines with large datasets.
• Lead initiatives in Data Engineering (DE) and Machine Learning Operations (ML Ops), encompassing pipeline management, executions, and monitoring data and model drift.
• With support, add value by translating model results into the “so-what’s” for clients.
• Ensure the delivery of client projects on time and within budget, while meeting or exceeding established quality standards.
• Directly manage communication with client stakeholders, providing guidance, consulting, and ensuring a clear understanding of project progress.
• Bachelor’s/Master’s Degree and minimum 6+ years of professional experience is required
• Degree in applied math, statistics, machine learning or computer science. PhD/ MS is preferred
• Deep understanding of statistics and experience with machine learning algorithms/techniques
• Fluency in healthcare data and ML programming languages in particular SQL, Python (pyspark), dbt, ML Flow strong experience with DL frameworks such as TensorFlow, Kedro and others
• Scientific expertise and real-world experience in deep learning (CNN, LSTM, NLP) & ML models.
• Experience with AWS services, Databricks, Snowflake, software DevOps CI/CD tools, GitLab.
• Preferred to have experience with healthcare data,
• e.g., clinical trial data, electronic medical records, and insurance claims; or Biosciences data, e.g., protein or small molecule data, or bioinformatics; or Biopharmaceutical manufacturing.
• Facilitate ML & Data engineering efforts by architecting and guiding the implementation of data and ML pipelines for development and deployment
• Experience working with non-technical stakeholders and developing clear, concise presentations that outline methodology, study results, and actionable insights
• Familiarity with agile methodologies, and project tracking tools and experience managing end-to-end data science projects
• Ability to work productively with a cross-functional team (e.g., general consultants, database developers, software engineers), identify and resolve tough issues in a collaborative manner.
• Experience in applying machine learning techniques to real-world problems in a production environment
• Self-motivation, initiative, innovation, appropriately independent, and willing to go above the call of duty
• Utilize expertise in integrating and leveraging Gen AI LLMs to maximize operational efficiency.
• Conduct test and control analysis and implement optimization algorithms (e.g., Genetic Algorithms).