Senior Data Scientist

Philadelphia, PA

Hybrid Role

Compensation: $163,000 - $173,000

We are seeking a highly skilled and experienced Lead or Principal Data Scientist to join our dynamic Data Science team. This is a senior, hands-on role responsible for providing technical leadership, scientific oversight, and mentorship to a team of talented data scientists. You will be instrumental in driving the scientific rigor of our machine learning projects, from ideation to deployment, ensuring they align with industry-standard governance and project management frameworks. The ideal candidate is a seasoned expert in building and deploying machine learning models, with a passion for guiding and developing junior team members. You will collaborate closely with our ML Engineer, MLOps Engineer, Data Engineer, and infrastructure teams to ensure our data scientists have the robust data and technical environment needed to innovate and deliver impactful solutions.

Key Responsibilities:

• Technical Leadership and Mentorship: Provide scientific oversight and guidance to data scientists on the team. This includes leading ideation sessions on new project approaches, reviewing initial exploratory data analysis (EDA), and offering expert input on feature selection, feature development, model building, and model selection.
• Scientific Best Practices and Governance: Champion and implement best practices for the full data science lifecycle. Ensure project execution aligns with the CRISP-DM methodology and that all projects adhere to the principles of the NIST AI Risk Management Framework (AI RMF) to ensure responsible, ethical, and governable AI development.
• Hands-On Model Development: Act as a significant individual contributor by building, experimenting with, and deploying sophisticated machine learning models (e.g., classification and regression) that are widely used throughout the business.
• Innovative Feature Engineering: Explore and apply advanced techniques for feature development, including the use of Generative AI to create novel features for traditional machine learning models.
• Cross-Functional Collaboration: Serve as a key technical liaison, interfacing seamlessly with our ML Engineer, MLOps Engineer, and Data Engineers to ensure smooth model deployment and productionalization. You will also partner with infrastructure teams to guarantee that the data science team has the necessary data access and technical stack to perform their work efficiently.
• Project Ideation and Scoping: Work with business stakeholders and the data science team to identify high-impact business problems and translate them into well-defined data science projects.
• Fostering Innovation: Stay current with the latest advancements in data science, machine learning, and generative AI, and encourage a culture of continuous learning, experimentation, and innovation within the team.

Qualifications:

• Education: Master's degree or Ph.D. in Computer Science, Statistics, Mathematics, Engineering, or a related quantitative field.
• Experience:
  • 7+ years of hands-on experience in data science, with a proven track record of building and deploying impactful machine learning models in a production environment.
  • Extensive experience working with complex domain data is strongly preferred.
  • Demonstrated experience in a senior or lead role, including mentoring junior data scientists and providing technical oversight on projects.
• Technical Skills & Knowledge:
  • Expert-level proficiency in Python and SQL.
  • Deep, hands-on experience with machine learning frameworks and libraries (e.g., Scikit-learn, TensorFlow, PyTorch).
  • Proven experience with cloud platforms, preferably Google Cloud Platform (GCP), and tools such as Vertex AI and BigQuery.
  • Familiarity with applying data science project methodologies (e.g., CRISP-DM) and AI governance frameworks (e.g., NIST AI Risk Management Framework).
  • Experience with MLOps principles and collaborating with engineering teams to deploy and monitor models.
  • Some experience with or a strong interest in generative AI, particularly its application in feature development for traditional machine learning models.
• Leadership and Collaboration:
  • Exceptional problem-solving skills with the ability to think creatively and strategically about complex business and technical challenges.
  • Excellent communication and interpersonal skills, with the ability to explain complex scientific concepts to both technical and non-technical audiences.
  • A collaborative spirit and the ability to work effectively in a team-oriented environment, fostering the growth and development of colleagues.
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