Top Senior Machine Learning Jobs in Philadelphia, PA

This role involves applying advanced data science and machine learning methods to healthcare data, leading teams, managing client relations, and developing analytics products for payers, while mentoring junior staff.

As a Sr. Data Analyst, you will gather requirements, manage projects, develop automation tools, create reports, and improve processes using data analytics and automation technologies.

As a Staff Data Scientist, you will lead product strategy by building predictive models, optimizing matching algorithms, and guiding product innovations in a consumer tech environment.

As a Principal Data Analyst, you'll leverage machine learning and AI to improve outcomes, manage large data sets, and guide analytics modelers for effective data solutions.

The Data Product Manager - Curation designs and manages curated data products, overseeing data modeling, governance, and partnership with analytics teams to enhance data usability and support advanced analytics, including Machine Learning.

The Senior Manager of Data Insights will lead a team to optimize data sharing and governance, develop data strategies, and deliver actionable business intelligence across Docker's operations.

The Procurement Analytics Lead analyzes procurement data using AI and analytics tools, creates reports, identifies process improvements, and collaborates with stakeholders to drive efficiency and compliance.

Lead marketing data science efforts, overseeing analytics and machine learning strategies to enhance user acquisition, engagement, and retention. Mentor a team and communicate data insights to executives to influence marketing and budget decisions.

The Manager of RevOps/Yield analyzes patterns and data to optimize ad operations, manage vendor relationships, and oversee a remote team for revenue generation.

Design, build, and deploy end-to-end data science solutions for pricing, underwriting, claims, and operations. Perform analyses on structured and unstructured data, develop and monitor ML models, create dashboards and visualizations, implement data pipelines, and collaborate cross-functionally to prioritize analytics initiatives and communicate results to technical and non-technical stakeholders.

The Senior Manager of Data Engineering leads the enterprise data platform team, overseeing data processing, delivering solutions, and mentoring team members, while aligning technical strategies with business objectives.

The Manager, Growth Analytics & Marketing Science will build and evolve marketing measurement frameworks, drive analytics, and optimize marketing strategies through data and AI.

As a Senior Machine Learning Engineer, you'll build and deploy ML models for risk and fraud detection, leading technical efforts and collaborating cross-functionally.

The Director of Statistics leads statistical initiatives in phase 3b/4 and RWE studies, provides expertise in HTA and regulatory compliance, and collaborates with cross-functional teams. Responsibilities include statistical analysis plans, Bayesian methodologies, and mentoring junior staff.

Design and implement credit strategies using data, machine learning, A/B testing and advanced analytics to optimize underwriting, pricing, and credit line decisions. Lead end-to-end analytics for credit card products, monitor portfolio performance, conduct root-cause analysis, partner cross-functionally, and communicate findings to stakeholders.

Lead a team of analysts to advance fraud and safety operations through analytics, ensuring robust data products and compliance while empowering stakeholders with actionable insights.

The Senior Data Scientist will design and deploy time-series forecasting models, collaborate with stakeholders, and operationalize models in cloud environments, focusing on demand planning across various industries.

Serve as an AI/ML-focused solution engineer for Healthcare & Life Sciences: provide technical guidance, design cloud data and lakehouse architectures on Snowflake, lead demos and proofs-of-concept, support sales cycles, collaborate with product and engineering, and communicate AI/ML value to technical and executive stakeholders.

Manage a team of data scientists to develop fraud detection models, lead planning and collaboration with leadership, and communicate outcomes effectively while maintaining strong business intuition and technical leadership.

Lead the design and operation of Twilio's ML and data platform, architecting cloud-native pipelines and implementing MLOps best practices while mentoring engineers and collaborating across teams.