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Synthetic Data Generation Jobs in Fall River, MA

Varsity Tutors

Organic Chemistry 2 Tutor

Providence, RI · Remote

$18 - $40/hr

... generation, and engagement features, helping you save prep time and focus on impactful teaching ... Guides students through designing multistep synthesis routes, predicting regiochemistry and ...

Varsity Tutors

Organic Chemistry Tutor

Providence, RI · Remote

$18 - $40/hr

... generation, and engagement features, helping you save prep time and focus on impactful teaching ... Guides students through structure determination from spectral data, multi-step synthesis planning ...

Varsity Tutors

Claude Tutor

Providence, RI · Remote

$18 - $40/hr

... generation, and engagement features, helping you save prep time and focus on impactful teaching ... synthesis, code review, writing feedback, and data interpretation. Emphasizes critical thinking ...

Varsity Tutors

AP Biology Tutor

Providence, RI · Remote

$18 - $40/hr

... generation, and engagement features, helping you save prep time and focus on impactful teaching ... synthesis, and signal transduction pathways. Guides students through experimental design, data ...

All JobsSynthetic Data Generation JobsSynthetic Data Generation Jobs in Fall River, MA

Synthetic Data Generation information

See Fall River, MA salary details

$31.1K

$93.6K

$169.6K

How much do synthetic data generation jobs pay per year?

As of Sep 6, 2026, the average yearly pay for synthetic data generation in Fall River, MA is $93,556.00, according to ZipRecruiter salary data. Most workers in this role earn between $54,700.00 and $145,100.00 per year, depending on experience, location, and employer.

What is synthetic data generation?

Synthetic data generation is the process of creating artificial datasets that mimic real-world data. This technique is used to supplement or replace actual data for purposes such as machine learning, software testing, and research, especially when real data is scarce, sensitive, or costly to obtain. Synthetic data can help improve model accuracy, protect privacy, and enable innovation by providing diverse and unbiased datasets. It is commonly used in fields like healthcare, finance, and autonomous vehicles.

What are the key skills and qualifications needed to thrive in synthetic data generation?

To excel in a Synthetic Data Generation role, you need a solid background in computer science, statistics, and data science, often supported by a relevant degree and experience in machine learning. Familiarity with tools such as Python, TensorFlow, PyTorch, and synthetic data generation platforms, as well as knowledge of privacy-preserving techniques, is typically required. Strong problem-solving abilities, creativity, and effective communication set top performers apart in this field. These skills and qualities are crucial for creating high-quality, realistic synthetic datasets that support robust AI model development while safeguarding sensitive information.

What are the main challenges faced by professionals working in synthetic data generation, and how can they be addressed?

Professionals in synthetic data generation often encounter challenges such as ensuring the generated data accurately represents real-world scenarios while maintaining privacy and data security. Balancing realism with anonymization is crucial, especially when synthetic data is used for AI model training or testing. Collaboration with data scientists, domain experts, and privacy officers is common to validate data utility and compliance with regulations. Staying current with advances in generative models and data validation techniques also helps address these challenges and contributes to career growth in this rapidly evolving field.

What is the difference between Synthetic Data Generation vs Data Analyst?

AspectSynthetic Data GenerationData Analyst
Required CredentialsKnowledge of data science, programming, and data privacyDegree in statistics, data science, or related field
Work EnvironmentData science teams, research labs, tech companiesBusiness environments, analytics teams, consulting firms
Industry UsageAI development, machine learning, data privacyBusiness insights, reporting, decision-making
Search & Comparison IntentUnderstanding data generation techniques, privacy solutionsAnalyzing data, generating reports, insights

While Synthetic Data Generation focuses on creating artificial data for privacy and model training, Data Analysts interpret existing data to provide business insights. Both roles require data-related skills but serve different purposes within the data ecosystem.

What job categories do people searching Synthetic Data Generation jobs in Fall River, MA look for?

The top searched job categories for Synthetic Data Generation jobs in Fall River, MA are:

What cities near Fall River, MA are hiring for Synthetic Data Generation jobs?

Cities near Fall River, MA with the most Synthetic Data Generation job openings:

Synthetic Data Generation jobs near you

Director, Clinical Data & AI

SMITH & NEPHEW SNATS INC

Mansfield, MA • On-site, Remote

Full-time

Medical, Dental, Vision, Retirement, PTO

Re-posted 24 days ago

Key responsibilities

  • Oversee the strategy, architecture, and operational execution of clinical data and AI capabilities supporting evidence generation.

  • Manage the design, governance, and evolution of clinical data platforms, data pipelines, and interoperability frameworks.

  • Lead the development and deployment of AI/ML capabilities across the clinical lifecycle, including data quality automation, analytics, and integration into workflows.

Job description

Life Unlimited. At Smith+Nephew we design and manufacture technology that takes the limits off living. The Director of Clinical Data & AI is the global functional leader responsible for the strategy, architecture, and operational execution of clinical data and AI capabilities supporting end-to-end evidence generation. This role owns the clinical data lifecycle-from data acquisition and management to advanced analytics, AI enablement, and synthetic/simulated data-ensuring all data assets are high-quality, interoperable, and fit-for-purpose for regulatory, scientific, and operational decision-making. The Director serves as the enterprise authority on clinical data platforms and AI-enabled evidence generation, driving integration across clinical systems, data engineering, AI/ML, and statistical/clinical programming. This position has full accountability for the strategy, execution, quality, and evolution of the Clinical Data & AI function globally.

What will you be doing?

1. Global Clinical Data & AI Strategy

  • Define and execute the global strategy for Clinical Data & AI aligned to enterprise evidence-generation and AI transformation goals
  • Establish a unified operating model integrating:
    • Clinical systems (EDC, eCOA, registries)
    • Clinical Data Lake & central data model
    • Data management and data engineering
    • AI/ML and advanced analytics
  • Serve as the enterprise authority on clinical data architecture and AI enablement for clinical & medical affairs across all BUs and geographies
  • Partner with Clinical Study Management, Clinical Strategy, Regulatory, Medical Affairs, Statistics, and IT to define data-driven evidence strategies

2. Clinical Data Architecture & Platforms

  • Own the design, governance, and evolution of:
    • Clinical Data Lake (CDL) and standardized data models
    • Clinical systems ecosystem (EDC, eCOA, registry ingestion, integrations)
    • Data pipelines, transformation, and interoperability frameworks
  • Ensure scalable, compliant, and extensible architecture supporting:
    • Cross-study analytics
    • Real-world data integration
    • Device + clinical data linkage
  • Drive standardization (e.g., CDISC-based models) and elimination of data silos

3. AI, Data Science & Advanced Analytics

  • Lead development and deployment of AI/ML capabilities across the clinical lifecycle, including:
    • Data quality automation and monitoring
    • AI-assisted clinical study reporting and analytics
    • Cross-study insights and meta-analyses
  • Drive integration of AI into core workflows, not point solutions
  • Establish best practices for:
    • Model development, validation, monitoring
    • Responsible AI (traceability, reproducibility, regulatory alignment)
  • Oversee collaboration between data science, statistics, and programming teams

4. Synthetic Data, Simulation & Virtual Twins

  • Own strategy and execution for:
    • Synthetic clinical data generation
    • Simulation frameworks for study design and operational planning
    • Virtual twin development for patient- and study-level modeling
  • Ensure alignment with regulatory expectations for transparency and scientific validity
  • Integrate synthetic and simulated data into:
    • Study design optimization
    • Evidence generation (e.g., hybrid designs, external controls)

5. Clinical Data Management & Quality

  • Oversee global clinical data management function, ensuring:
    • High-quality, consistent, and inspection-ready data
    • Efficient study startup (eCRF design, database builds) and closeout
    • Risk-based monitoring and analytics-driven data review
  • Embed AI, machine learning modeling, and automation into CDM workflows to improve efficiency and quality
  • Ensure alignment with regulatory and compliance standards (FDA, EU MDR, GDPR, HIPAA)

6. Statistical & Clinical Programming Integration

  • Own alignment and integration of:
    • Statistical programming (TFLs, ADaM outputs)
    • Clinical programming (data pipelines, transformations)
  • Ensure seamless data flow from raw data analysis-ready datasets reporting
  • Drive standardization, automation, and reuse across studies and programs
  • Leverage AI solutions to accelerate programming across Global Clinical and Medical Affairs

7. Operational Excellence & Delivery Model

  • Own intake, prioritization, and delivery across:
    • Data platform initiatives
    • AI/ML programs
    • Study-level data operations
  • Implement scalable delivery models for standardized multi-source clinical outcomes datasets from the Clinical Data Lake to key business stakeholder teams
  • Optimize resourcing across:
    • High-throughput standardized work
    • High-complexity AI/data science initiatives

8. Regulatory & Data Governance Leadership

  • Ensure all clinical data and AI activities are:
    • Compliant with global regulatory requirements
    • Traceable, auditable, and reproducible
  • Establish strong governance across:
    • Data standards and lineage
    • AI model lifecycle
    • Data privacy and security
  • Support regulatory submissions with robust, defensible data strategies

Key Interfaces

  • Global Clinical Research Operations leadership
  • Clinical / Medical Affairs / Regulatory Affairs
  • Statistics, Data Science, and AI teams
  • IT / Digital / Enterprise Data organizations
  • External partners, CROs, AI vendors, and regulators

Education

  • BA required, PhD (preferred) or Master's in Data Science, Biostatistics, Computer Science, or related field

What will you need to be successful?

  • Minimum of 10 years experience across clinical data, AI/ML, and data platforms in medtech/pharma/biotech
  • Proven leadership of multi-domain teams (data management, engineering, data science, AI, programming)
  • Demonstrated ownership of enterprise data architecture (e.g., data lake/platform) - Databricks preferred
  • Strong track record supporting regulatory submissions and clinical evidence generation
  • Enterprise mindset - integrates data, AI, and operations into a unified capability
  • Technical depth + breadth - credible across data engineering, CDM, AI, and analytics
  • Regulatory credibility - understands how data and AI decisions impact submissions
  • Execution rigor - delivers scalable, high-quality platforms and outputs
  • Transformational leadership - embeds AI into workflows, not as isolated innovation
  • Pragmatic innovation - advances capabilities while maintaining compliance and reliability

You Unlimited.

  • The anticipated base compensation range for this position is $165,250-$236,000USD annually. The actual base pay offered to the successful candidate will be based on multiple factors, including but not limited to job-related knowledge/skills, experience, and geographic location. Compensation decisions are dependent upon the facts and circumstances of each position and candidate. In addition to base pay, we offer competitive bonus and benefits, including medical, dental, and vision coverage, 401(k), tuition reimbursement, medical leave programs, parental leave, generous PTO, paid company holidays, 8 hours of volunteer time annually, and a variety of wellness offerings such as EAP.

  • Inclusion + Belonging: Committed to Welcoming, Celebrating and Thriving. Learn more about our Employee Inclusion Groups on our website https://www.smith-nephew.com/

  • Your Future: 401k Matching Program, 401k Plus Program, Discounted Stock Options, Tuition Reimbursement

  • Work/Life Balance: Flexible Personal/Vacation Time Off, Paid Holidays, Flex Holidays, Paid Community Service Day

  • Your Wellbeing: Medical, Dental, Vision, Health Savings Account (Employer Contribution of $500+ annually), Employee Assistance Program, Parental Leave, Fertility and Adoption Assistance Program

  • Flexibility: Hybrid Work Model (For most professional roles)

  • Training: Hands-On, Team-Customized, Mentorship

  • Extra Perks: Discounts on fitness clubs, travel and more!

Smith+Nephew provides equal employment opportunities to applicants and employees without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, protected veteran status, or disability.

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