Data Scientist

Data Scientist (Data Science + Data Engineering)

Location: Remote (Preference for Northeast/Mid-Atlantic; monthly travel to Plymouth Meeting, PA as needed)

Our client is building a data-driven culture where analytics, AI, and technology directly influence business decisions. They are seeking a Data Scientist who can translate complex business challenges into actionable insights while helping bring advanced analytics and machine learning solutions into production.

This role is ideal for someone who enjoys the full lifecycle of data science-from exploring data and building predictive models to partnering with engineering teams to deploy solutions that create measurable business value. The successful candidate will work closely with data engineers, business stakeholders, and executive leadership to help shape the future of analytics and AI across the organization.

While this is primarily a Data Science role, candidates should have a solid understanding of data engineering concepts and how models are operationalized within modern data ecosystems.

Responsibilities:

Data Science & Advanced Analytics

• Analyze large and complex datasets to identify patterns, trends, and opportunities that support strategic business decisions.
• Develop predictive models, scoring frameworks, and machine learning solutions that enhance business performance and decision-making.
• Apply statistical and analytical techniques to solve real-world business problems and uncover actionable insights.
• Continuously evaluate model effectiveness and recommend enhancements based on business outcomes and evolving data.

AI & Innovation

• Contribute to the organization's growing AI strategy, including opportunities to leverage generative AI and emerging technologies.
• Explore innovative approaches to automation, decision support, and workflow optimization through advanced analytics and AI solutions.
• Partner with leadership to identify high-value use cases where AI can improve operational efficiency and decision quality.

Data Engineering Collaboration

• Work closely with data engineering teams to ensure analytical solutions can be deployed, maintained, and scaled effectively.
• Collaborate on the design and implementation of data pipelines that support machine learning and advanced analytics initiatives.
• Help bridge the gap between model development and production deployment by ensuring solutions are practical, reliable, and business-ready.
• Support efforts to improve data quality, accessibility, and governance across the organization.

Business Partnership

• Engage directly with business stakeholders to understand challenges, define analytical approaches, and deliver impactful solutions.
• Translate technical findings into clear, concise recommendations for both technical and non-technical audiences.
• Serve as a trusted partner in helping business leaders make data-informed decisions.

Qualifications

Required

• 3+ years of hands-on experience in Data Science, Analytics, Machine Learning, or a related quantitative field.
• Proven experience developing predictive models or machine learning solutions in a business environment.
• Strong proficiency in Python and modern data science libraries.
• Advanced SQL skills and experience working with large-scale, complex datasets.
• Experience applying statistical analysis and predictive modeling techniques to business problems.
• Understanding of data engineering concepts, including data pipelines, model deployment, and production environments.
• Experience working with cloud-based platforms such as Azure, AWS, or Google Cloud.
• Strong communication and stakeholder management skills with the ability to explain technical concepts to business audiences.

Preferred

• Experience collaborating closely with Data Engineering teams or supporting machine learning deployment initiatives.
• Familiarity with distributed computing tools such as Spark or PySpark.
• Experience developing AI-driven solutions, including Generative AI, Large Language Models (LLMs), or agent-based workflows.
• Background in insurance, financial services, or other highly regulated industries.
• Experience building production-grade machine learning applications.
• Master's or PhD in Data Science, Statistics, Computer Science, Mathematics, Engineering, or a related quantitative discipline.

The ideal candidate:

• Enjoys solving complex business problems through data.
• Approaches challenges with curiosity and asks thoughtful questions.
• Can balance analytical rigor with practical business impact.
• Is comfortable working independently while collaborating across teams.
• Takes ownership and drives projects from concept through implementation.
• Learns new technologies and business domains quickly.
• Values building solutions that can be used and adopted by the business-not just creating models.