Data Scientist

• Lead the design, development, and enhancement of forecasting models to improve prediction accuracy and business outcomes.
• Build and deploy advanced Deep Learning models for large-scale structured and unstructured datasets.
• Develop Generative AI / Explainable AI solutions to provide transparent and interpretable insights from predictive models.
• Analyze healthcare datasets including claims, patient, provider, operational, or clinical data.
• Collaborate with business stakeholders, product teams, and engineering teams to translate business challenges into scalable AI solutions.
• Monitor model performance, retrain models, and optimize algorithms for production environments.
• Mentor junior data scientists and provide technical leadership across the project.
• Ensure compliance with healthcare data privacy and governance standards.

Lead Data Scientist – Healthcare

• Analyze large, complex fraud datasets to identify patterns, trends, and anomalies that inform detection strategies and business decisions
• Evaluate existing static, rule-based fraud detection systems through data-driven assessments of their performance and coverage, and deliver clear, prioritized recommendations for rule updates, retirement, or new rule creation
• Partner with fraud operations teams to understand frontline detection challenges and translate operational insights into analytical hypotheses and actionable solutions
• Utilize AI-assisted development tools such as Amazon Q and Kiro to accelerate analytical workflows and solution delivery
• Prototype and contribute to the development of agentic AI applications leveraging AWS AgentCore and generative AI solutions that advance the team's fraud strategy capabilities
• Collaborate with fraud technology teams to ensure models, rules, and AI-driven outputs are implemented accurately and monitored effectively within the AWS production environment
• Design, build, and validate machine learning and statistical models to enhance fraud detection capabilities, improve precision and recall, and reduce false positive rates
• Monitor deployed models and fraud rules on an ongoing basis, identifying performance degradation or emerging detection gaps that require intervention
• Communicate findings, model results, and strategic recommendations clearly to both technical and non-technical stakeholders
• Stay current with emerging trends in fraud typologies, financial crime, and AI and machine learning developments within the AWS ecosystem, and bring relevant innovations back to the team

Lead Data Scientist – Gen AI Lead

• Manage a group or process(potentially global)
• Work with business data users to define the use of data within risk systems
• Recommend remediation of control gaps and implements effective risk controls
• Participate in and lead user acceptance testing of data control processes
• Ensure the completeness, validity, and accuracy of data on a daily basis
• Lead cross functional strategic efforts to remediate data quality issues
• Evaluate existing GRM data architecture and design solutions to simplify and optimize the data environment
• Participate in building the business cases required to secure approval and funding for the Data and Analytical efforts

Data Support Manager

• Lead the strategy, development roadmap, and lifecycle of data products and platforms
• Define the roadmap for data solutions
• Collaborate with cross-functional teams
• Ensure data products are scalable, reliable, and user-friendly
• Monitor and improve data quality, reliability, and user adoption of data tools
• Define and track success metrics for data products

Staff Data Product Manager

• Lead and contribute to the design of observational database analysis, including authoring protocols and analysis plans in support of RWE needs for the MedTech Cardiovascular franchise.
• Lead the synthesis of evidence from observational database analyses with other evidence to inform medical decision-making, including authoring publications and presentations.
• Work closely with colleagues within the department and across the larger cross-sector Epidemiology function, health economics and market access (HEMA) function, clinical function, and other Cardiovascular medical device functions.
• Implement new approaches for working with “big data” in healthcare. Some tasks may include data visualization, predictive analytics and machine learning algorithms.

Associate Director, MedTech Epidemiology, Real World Data Science

• Lead and contribute to the design of observational database analysis, including authoring protocols and analysis plans in support of RWE needs for the MedTech Cardiovascular franchise.
• Lead the synthesis of evidence from observational database analyses with other evidence to inform medical decision-making, including authoring publications and presentations.
• Work closely with colleagues within the department and across the larger cross-sector Epidemiology function, health economics and market access (HEMA) function, clinical function, and other Cardiovascular medical device functions.
• Implement new approaches for working with “big data” in healthcare. Some tasks may include data visualization, predictive analytics and machine learning algorithms.
• The position will report to the Senior Director, MT EPI-RWDS.

• Design the lab of the future by developing technologies that integrate robotics, analytical instrumentation, and software into cohesive, high-performing solutions
• Deliver world-renowned science by partnering with researchers to understand experimental needs and convert them into scalable digital workflows
• Work at the intersection of multiple disciplines, collaborating with experts in automation, data science, modeling, IT, and the CMC community at large
• Lead high-impact projects from concept through deployment, working across teams and stakeholders to deliver meaningful outcomes
• Continuously improve existing digital technologies and automation platforms to enhance performance, usability, and reliability
• Empower others by providing hands-on partnership, support, troubleshooting, and training to scientists using these technologies

Principal Scientist, DSCS Digital Technologies – Laboratory Automation, Time Series Data Strategy

• Responsible for mining big data, interpreting and analyzing complex data sets
• Develops and deploys predictive models to analyze company performance and improve customer experiences
• Applies statistical techniques and machine learning algorithms to identify trends within Mazda data
• Collaborates closely with data engineers, analysts, and business stakeholders to integrate data-driven solutions

• Be a hands-on scientific and technical leader, leading a portfolio of RWE projects, developing best practices, and mentoring team members.
• Identify common technical requirements for RWE projects, develop and maintain a common suite of tools that enable RWE data scientists to accelerate project delivery.
• End-to-end expertise in RWE studies including conceptualizing research questions, data feasibility, study design, analysis, programming, and interpretation.
• Provide thought leadership and hands-on programming expertise for developing and applying Real-World Data (“RWD”) methodologies to mitigate observed and unobserved biases in the execution of external control arm studies, hybrid control studies, comparative effectiveness analyses, etc.
• Provide thought leadership and hands-on programming expertise to generate actionable insights from post-hoc RCT analyses, observational databases, and literature reviews to support regulatory agency interactions.
• Independently create study protocols, statistical analysis plans, and statistical programming deliverables including analysis-ready data, tables and figures
• Partner with the Data Science Therapeutic Area scientists to conceptualize, lead, shape and deliver Real World Evidence

Senior Principal Scientist, Data Science RWE

• Lead operational planning and execution of the enterprise AI Literacy strategy across multiple geographies and business functions.
• Develop and maintain scalable operating models, governance structures, processes, and delivery frameworks supporting AI Literacy initiatives.
• Manage the end-to-end delivery of virtual and in-person AI Literacy learning programs for approximately 15000 employees globally.
• Coordinate schedules, timelines, resources, dependencies, and implementation activities across multiple workstreams.
• Manage external vendors, agencies, and contractors responsible for training delivery and operational support.
• Monitor vendor performance, contractual obligations, service levels, and delivery outcomes.
• Manage program budgets, forecasts, resource plans, and operational expenditures.
• Drive continuous improvement initiatives focused on operational efficiency, scalability, and cost effectiveness.
• Implement processes and controls that support sustainable growth of the AI Literacy program.
• Lead continuous improvement efforts through data-driven decision making and performance analysis.

Director, R&D Data Science – Operations & Community Lead

• Be a hands-on scientific and technical leader, leading a portfolio of RWE projects, developing best practices, and mentoring team members.
• Identify common technical requirements for RWE projects, develop and maintain a common suite of tools that enable RWE data scientists to accelerate project delivery.
• End-to-end expertise in RWE studies including conceptualizing research questions, data feasibility, study design, analysis, programming, and interpretation.
• Provide thought leadership and hands-on programming expertise for developing and applying Real-World Data (“RWD”) methodologies to mitigate observed and unobserved biases in the execution of external control arm studies, hybrid control studies, comparative effectiveness analyses, etc.
• Provide thought leadership and hands-on programming expertise to generate actionable insights from post-hoc RCT analyses, observational databases, and literature reviews to support regulatory agency interactions.
• Independently create study protocols, statistical analysis plans, and statistical programming deliverables including analysis-ready data, tables and figures.
• Partner with the Data Science Therapeutic Area scientists to conceptualize, lead, shape and deliver Real World Evidence.

• Partner with cross-functional teams to define a neuropsychiatric digital health roadmap that integrates AI/ML (e.g., foundation models), data science, and company priorities across the patient journey, ensuring clinical relevance and patient-centered outcomes.
• Monitor emerging technologies in neuropsychiatry, digital phenotyping, remote assessment, passive sensing, and speech and language analytics to bring forward next-generation solutions for neuropsychiatry R&D.
• Execute the neuropsychiatry-focused digital health strategy by applying AI/ML, modeling, and longitudinal analytics to support patient stratification, endpoint development, and clinical trial optimization, in partnership with disease experts, statisticians, and platforms and clinical teams on study design and validation of digital measures, balancing their informativeness with low clinical burden for patients and study teams.
• Develop and apply sophisticated statistical/ML/AI models using digital health data to generate insights into disease and treatment outcomes.
• Develop multimodal approaches combining digital health data with imaging, blood-based biomarkers, omics, and real-world data to provide a more holistic picture of patients suffering from neuropsychiatric disease.
• Contribute to evidence packages to support regulatory qualification of digital endpoints and tools.
• Ensure validation and compliance with evolving global regulatory and data privacy standards.
• Collaborate with clinical operations to integrate digital health technologies into trial design, working cross-functionally to optimize data capture, integration, and interoperability of digital solutions.
• Partner with regulatory and medical affairs to build external collaborations with academic groups, technology partners, regulators, health authorities, and payers to advance digital health standards and novel endpoints.

Senior Manager, R&D Data, Data Science, AI – Digital Health for Neuropsychiatry

• Lead end-to-end AI/ML and GenAI projects, from business problem framing and requirements gathering through data preparation, model development, solution architecture, production deployment, and post-production monitoring and optimization
• Architect and deliver comprehensive ML-based solutions for highly complex, cross-functional projects that address diverse challenges and opportunities across J&J's business segments
• Design, build, and deploy agentic AI systems, including multi-agent orchestration, tool-use frameworks, retrieval-augmented generation (RAG), and autonomous decision-making workflows
• Conduct proof-of-concepts (POCs) and rapidly prototype ML/GenAI-based digital products aligned with business priorities across various segments and functions, translating POCs into production-grade solutions
• Collaborate closely with product management, engineering, and business partners to define product vision, develop technical roadmaps, and ensure AI solutions deliver measurable business value
• Provide technical leadership and mentorship to data scientists and ML engineers; conduct code reviews, model reviews, and establish best practices for the team
• Author and review technical proposals, design documents, and reports; contribute to internal knowledge sharing and external thought leadership in AI/ML
• Stay at the forefront of the AI/ML landscape, continuously evaluate emerging technologies (foundation models, agentic frameworks, multi-modal AI) and drive their adoption to evolve the broader Data Science team's capabilities
• Establish and enforce standards for model governance, responsible AI practices, reproducibility, and documentation across the project lifecycle

Lead Data Scientist

• Partner with cross-functional teams to define a neuropsychiatric digital health roadmap that integrates AI/ML (e.g., foundation models), data science, and company priorities across the patient journey, ensuring clinical relevance and patient-centered outcomes.
• Monitor emerging technologies in neuropsychiatry, digital phenotyping, remote assessment, passive sensing, and speech and language analytics to bring forward next-generation solutions for neuropsychiatry R&D.
• Execute the neuropsychiatry-focused digital health strategy by applying AI/ML, modeling, and longitudinal analytics to support patient stratification, endpoint development, and clinical trial optimization, in partnership with disease experts, statisticians, and platforms and clinical teams on study design and validation of digital measures, balancing their informativeness with low clinical burden for patients and study teams.
• Develop and apply sophisticated statistical/ML/AI models using digital health data to generate insights into disease and treatment outcomes.
• Leverage longitudinal disease modeling, Bayesian methods, and causal inference techniques to inform decision-making.
• Develop multimodal approaches combining digital health data with imaging, blood-based biomarkers, omics, and real-world data to provide a more holistic picture of patients suffering from neuropsychiatric disease.
• Contribute to evidence packages to support regulatory qualification of digital endpoints and tools.
• Ensure validation and compliance with evolving global regulatory and data privacy standards.
• Collaborate with clinical operations to integrate digital health technologies into trial design, working cross-functionally to optimize data capture, integration, and interoperability of digital solutions.
• Partner with regulatory and medical affairs to build external collaborations with academic groups, technology partners, regulators, health authorities, and payers to advance digital health standards and novel endpoints.

• Developing complex national and regional sales and marketing campaigns across a range of services within Verizon Business Group (VBG) to drive revenue growth
• Using your skills in SQL, Python, artificial intelligence & other analytics methods to explore & analyze data in multiple forms, sources & platforms to provide meaningful insights & recommend actions that drive results
• Assist in the strategic development of national and regional marketing campaigns, both as standalone initiatives and within broader customer journey frameworks
• Extract data points based off Marketing requirements while working with marketing and sales operations to fulfill a complete life cycle of a marketing and sales campaign
• Assist in a variety of projects within the Marketing Science team focused on automating and simplifying processes, while also providing ad hoc data manipulation and analysis to support marketing and strategy on sales plays
• Provide full quality analysis of results and output prior to delivery to Sales Operations and marketing managers as well as follow up compliance regulation to legal
• Demonstrate high-level strategic thinking by continuously aligning technological and data solutions with overall business strategies

Marketing Data Scientist

• Leading the use of data to make decisions with a focus on supporting the Casualty business through predictive modeling and machine learning solutions.
• Developing and recommending new machine learning predictive modeling algorithms.
• Coding/developing tools that use machine learning/predictive modeling to make business decisions.
• Searching for and integrating new data to improve modeling and machine learning results.
• Discovery of new business problems that can be solved through the use of machine learning/predictive modeling.
• Mentoring and developing junior data scientists and managing complex projects as an individual contributor.

Lead Data Scientist, Consultant

• Lead and contribute to the design of observational database analysis, including authoring protocols and analysis plans in support of RWE needs for the MedTech Cardiovascular franchise.
• Lead the synthesis of evidence from observational database analyses with other evidence to inform medical decision-making, including authoring publications and presentations.
• Work closely with colleagues within the department and across the larger cross-sector Epidemiology function, health economics and market access (HEMA) function, clinical function, and other Cardiovascular medical device functions.
• Implement new approaches for working with “big data” in healthcare.

Associate Director, MedTech Epidemiology, Real-World Data Sciences

• Lead and contribute to the design of observational database analysis, including authoring protocols and analysis plans in support of RWE needs for the MedTech Cardiovascular franchise.
• Lead the synthesis of evidence from observational database analyses with other evidence to inform medical decision-making, including authoring publications and presentations.
• Work closely with colleagues within the department and across the larger cross-sector Epidemiology function, health economics and market access (HEMA) function, clinical function, and other Cardiovascular medical device functions.
• Implement new approaches for working with “big data” in healthcare.
• Some tasks may include data visualization, predictive analytics and machine learning algorithms.

Data Scientist jobs in New Jersey

See all jobs on JobTailor

Lead Data Scientist – Healthcare

Lead Data Scientist – Gen AI Lead

Data Support Manager

Staff Data Product Manager

Associate Director, MedTech Epidemiology, Real World Data Science

Associate Director, MedTech Epidemiology, Real World Data Science

Principal Scientist, DSCS Digital Technologies – Laboratory Automation, Time Series Data Strategy

Data Scientist

Senior Principal Scientist, Data Science RWE

Director, R&D Data Science – Operations & Community Lead

Senior Principal Scientist, Data Science RWE

Director, R&D Data Science – Operations & Community Lead

Senior Manager, R&D Data, Data Science, AI – Digital Health for Neuropsychiatry

Lead Data Scientist

Senior Manager, R&D Data, Data Science, AI – Digital Health for Neuropsychiatry

Lead Data Scientist

Marketing Data Scientist

Lead Data Scientist, Consultant

Associate Director, MedTech Epidemiology, Real-World Data Sciences

Associate Director, MedTech Epidemiology, Real-World Data Sciences