Top Machine Learning Jobs in Philadelphia, PA

Leads the design and modernization of scalable data architecture for economic data platforms, including massive time-series datasets, archival documents, and research publications. Responsibilities include data modeling, metadata and governance design, storage and processing architecture, indexing, discoverability, security, lifecycle management, and API data access optimization. Partners with application architecture and cross-functional teams to deliver highly available, secure, public-facing data platforms supporting analytics and research.

Leads People Analytics enablement and BI strategy, overseeing semantic models, certified data products, metric governance, data foundations, advanced analytics enablement, demand prioritization, and adoption. Partners with People Analytics, IT, and HR Strategy teams to create trusted workforce data, reusable semantic assets, and decision-ready insights. Manages teams and operating models while driving data quality, governance, certification, self-service analytics, AI access, and scalable insight delivery across the enterprise.

Leverage advanced analytics, data manipulation, visualization, statistical modeling, predictive analytics, and machine learning to generate client insights and recommendations. Apply Adobe Experience Cloud solutions to support business transformation and strategic decision-making. Build client relationships, manage and inspire teams, navigate complex engagements, uphold professional standards, and deliver quality outcomes while developing technical and consulting expertise.

Leads data analysis and advanced analytics workstreams for clients, using statistical modeling, machine learning, data visualization, and Adobe Experience Cloud technologies to improve operations and strategic decision-making. Manages project planning, budgeting, execution, and completion while mentoring teams, maintaining data integrity and security, building Tableau and Power BI dashboards, identifying business opportunities, and integrating innovative analytics practices.

Architects and develops enterprise data platforms across cloud and on-premises environments. Designs scalable batch and real-time data pipelines, APIs, delivery services, and distributed applications. Establishes data management, quality, metadata, lineage, and integration practices while guiding technology selection and platform strategy. Collaborates with engineers, data scientists, analysts, and product owners in an agile environment.

Conducts econometric, causal, statistical, and optimization analyses to improve operational efficiency, resource allocation, product performance, and business outcomes. The role defines efficiency measures, develops forecasting and optimization tools, partners with product and engineering teams on data instrumentation and implementation, and communicates findings to executives through clear reports.

Analyzes operational performance and financial-impact data for Philadelphia’s Water Department. Responsibilities include acquiring and cleaning large datasets, building data models and views, applying statistical and machine learning methods, identifying patterns, automating predictive and prescriptive analytics, and communicating insights to stakeholders. The role supports resource allocation, asset management, leak detection, water-loss mitigation, and revenue integrity. It requires onsite work, Philadelphia residency, off-hours availability, and participation in an on-call rotation.

Support analytics by cleaning, validating, and transforming data; build dashboards and KPI reports; perform trend, cohort, and A/B testing analyses; improve data pipelines and forecasting; present insights to cross-functional teams.

Establish and lead the People Analytics function, building trusted workforce data foundations, governance, privacy standards, analytical products, workforce planning capabilities, and AI-enabled decision support. Partner across HR, Finance, IT, and business leadership to deliver executive insights, improve workforce decisions, and scale analytics operations. Recruit and develop the analytics team while ensuring responsible data use, global-local compliance, stakeholder adoption, and alignment with organizational priorities.

Design and implement scalable analytics/data models and dbt pipelines, manage Databricks, build dashboards and BI solutions, partner with business stakeholders, support CRM/marketing analytics, and explore GenAI/ML opportunities to deliver reliable, automated data products.

Lead data engineering and machine learning initiatives by designing, architecting, developing, and supporting GCP data pipelines. Create information architecture documentation, estimate and plan technical work, collaborate with project teams and business analysts, and translate requirements into technical designs. Develop Python-based ELT pipelines, optimize BigQuery SQL, support CI/CD, and provide technical leadership to projects and teams.

Build analytical frameworks, measurement methodologies, and decision support to drive enterprise decisions across marketing, loyalty, pricing, menu, media, and operations. Mine and integrate customer, transaction, digital, and operational data; develop segmentation, predictive models, and dashboards; lead test-and-learn, incrementality, and forecasting efforts; translate analysis into clear business recommendations and scalable decisioning tools.

Leads advanced analytics initiatives supporting Tribal healthcare programs and strategic decision-making. Develops predictive and prescriptive models, integrates complex data sources, establishes data quality frameworks, evaluates healthcare data resources, and produces reports and dashboards. Coordinates cross-functional projects, ensures federal compliance, supports quality measurement, mentors junior staff, and may serve as a team lead.

Build data foundations and end-to-end pipelines, research and implement quantitative pricing, market-making, and risk models for prediction markets, model cross-market dependencies and parlays, develop backtesting/simulation frameworks, monitor model performance and collaborate closely with traders to improve pricing and trading outcomes.

Analyze product, user, transactional, and repayment data to develop risk and underwriting models, reduce losses and fraud, monitor model performance, and guide product and growth strategy. Build Looker dashboards and LookML models, conduct segmentation and experimentation, measure CAC and LTV, and communicate actionable insights to product, engineering, marketing, finance, and leadership.

Supports Technology and Digital Quality initiatives through business process and data analysis, data ingestion, statistical analysis, reporting, dashboard development, automation, and AI validation. The intern prepares datasets, identifies insights, develops recommendations, documents processes, and communicates findings to technical and non-technical stakeholders. The role collaborates with quality, finance, operations, and other departments while contributing to continuous improvement and data-driven decision-making.

Build and operate scalable cloud data pipelines, warehouses, transformation layers, and ML production systems. Lead data infrastructure architecture, warehouse performance optimization, DataOps monitoring, CI/CD improvements, data quality testing, and large-scale data product delivery. Collaborate across teams, promote data stewardship, and support batch and streaming pipelines while deploying, serving, and monitoring machine learning models.

Leads and builds Instructure’s decision science function, including hiring, team development, roadmap ownership, experimentation, causal and predictive modeling, executive insights, and data literacy. Partners with product, engineering, marketing, finance, and executives to guide product, pricing, retention, go-to-market, and operational decisions. Establishes analytical standards, supports data infrastructure, and ensures ethical, privacy-conscious use of student data.

Leads end-to-end data architecture, engineering pipelines, semantic-layer development, data quality, monitoring, and platform operations. Manages agile sprint planning, release delivery, DevOps metrics, and cross-functional project plans. Establishes MLOps and machine-learning platform capabilities, mentors data engineers, manages team members, and ensures reliable, well-governed data solutions for business intelligence and reporting.

Leads end-to-end data architecture, engineering pipelines, ETL, data modeling, quality monitoring, and Tableau BI platform operations. Partners with stakeholders to define requirements, manage project plans, lead agile sprints, and deliver reliable data solutions. Establishes governance, security, performance, and continuous improvement practices while developing AI-enabled analytics capabilities. Mentors data engineers and manages team performance, documentation, dependencies, and delivery across multiple workstreams.

Leads marketing analytics, predictive modeling, segmentation, and experimentation to improve acquisition, retention, and pipeline performance. Designs and maintains machine learning models, governs A/B and multivariate testing, translates insights into campaign execution, and evaluates analytics platforms. Manages budgets, regulatory compliance, strategic initiatives, and team performance while partnering with Finance, Sales, Operations, IT, and servicing teams. The role is fully on-site with occasional adjusted schedules.

Lead CX data intelligence initiatives using SQL, dashboards, analytical models, and AI/LLM techniques. Analyze customer journeys, operational metrics, churn risks, and retention indicators; develop insight agents and self-service reporting; define CX health metrics; and translate findings into strategic recommendations, process improvements, and experiments. Partner with operations, support, product, data, and engineering teams while maintaining data quality, governance, and documentation.

Leads data engineering and data science teams, setting strategy and delivering scalable pipelines, data architecture, models, analytics infrastructure, and data governance. Partners with product, analytics, and business stakeholders to prioritize initiatives, assess upstream changes, maintain data quality, and support Snowflake-based warehousing. This hands-on leadership role includes mentoring staff, contributing to technical codebases, and advancing data science and engineering solutions.

Leads the enterprise data strategy, builds and scales the Data & Analytics team, and oversees data warehouse modernization, migration, governance, KPI standardization, and data quality. Partners with executives and cross-functional teams to convert data into actionable insights, operational improvements, predictive analytics, and AI-enabled workflows. The role requires hands-on Python, SQL, cloud, machine learning, and ETL expertise, plus strong leadership and communication skills.

Leads enterprise data, analytics, business intelligence, and AI strategy across healthcare payer functions. Partners with executives to prioritize analytics initiatives, build reporting and predictive insight capabilities, establish data governance and accountability, and drive measurable business outcomes. Oversees cross-functional teams, enterprise application development, Azure architecture, quality automation, DevOps, and intelligent automation while engaging C-suite and board stakeholders.