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Synthetic Data Generation Jobs in Indiana (NOW HIRING)

Physical AI Senior Manager

Indianapolis, IN · On-site

$120K - $159K/yr

Synthetic data generation and validation approaches for model development * Edge AI deployment (e.g., performance, reliability, lifecycle operations) * Manufacturing and supply chain domain ...

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

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

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

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

... 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 ...

... 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 ...

... 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 ...

... 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 ...

... 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 ...

$172K - $269K/yr

... generation, documentation, and test synthesis. * Legacy & Modern Languages: Deep expertise in ... Platforms & Data: Familiarity with both Windows and Linux environments and database technologies.

Claude Tutor

Valparaiso, IN · 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 ...

Claude Tutor

Fort Wayne, IN · 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 ...

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Synthetic Data Generation information

What are the key skills and qualifications needed to thrive in a Synthetic Data Generation role, and why are they important?

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 is the salary of a synthetic data engineer?

The salary of a synthetic data engineer typically ranges from $80,000 to $150,000 annually, depending on experience, location, and company size. Professionals with skills in data modeling, machine learning, and programming languages like Python or SQL tend to earn higher salaries.

Which 3 jobs will survive AI?

Synthetic Data Generation specialists are likely to continue being in demand as AI development requires high-quality, labeled data for training models. Roles involving data curation, domain expertise, and oversight of AI systems—such as data scientists, AI ethics officers, and machine learning engineers—are also expected to persist due to their specialized skills and the need for human judgment. These jobs often require technical knowledge, programming skills, and continuous learning to adapt to evolving AI technologies.

What is an example of synthetic data generation?

Synthetic data generation, relevant to roles like data scientists or AI engineers, involves creating artificial data that mimics real datasets using algorithms such as generative adversarial networks (GANs) or statistical models. For example, generating realistic customer transaction records for testing machine learning models without exposing sensitive information. This process helps improve model training while maintaining data privacy and security.

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 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 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.

Is 40 too late for data science?

Age is not a barrier to entering data science or synthetic data generation roles. Many professionals successfully transition into these fields later in life by acquiring relevant skills such as programming, statistics, and machine learning, often through online courses or certifications. Experience, continuous learning, and adaptability are valued more than age in the tech industry.
What job categories do people searching Synthetic Data Generation jobs in Indiana look for? The top searched job categories for Synthetic Data Generation jobs in Indiana are:
What cities in Indiana are hiring for Synthetic Data Generation jobs? Cities in Indiana with the most Synthetic Data Generation job openings:
Physical AI Senior Manager

Physical AI Senior Manager

Deloitte

Indianapolis, IN • On-site

$120K - $159K/yr

Other

Posted 3 hours ago


Deloitte rating

8.0

Company rating: 8.0 out of 10

Based on 89 frontline employees who took The Breakroom Quiz

71st of 146 rated financial services


Job description

Physical AI Senior Manager - Manufacturing & Supply Chain

We are a team of strategic advisors, architects, and implementers who drive business transformations. Our diverse talent energizes clients' business functions and technology to maximize value in Supply Chain enhancing their ability to fulfill their growth and efficiency ambitions. Imagine working with world-class supply network capabilities like Smart Factory, Strategy & Innovation, Supply Chain Responsiveness, Sourcing & Procurement, or Product Development & Operations!
Are you ready to take your career to new heights? Join our US Supply Chain & Network Operations Offering, where you'll deliver transformational solutions using operational expertise, digital technologies, advanced analytics, and industry-specific hybrid solutions. Don't miss the chance to be part of a team that provides exceptional client value while advancing your professional journey. Apply now and become a vital part of our innovative and dynamic workforce!

Recruiting for this role ends on 8/31/26.

The team

You will join a cross-functional Supply Chain & Manufacturing consulting environment focused on helping clients modernize operations through technology, data, and advanced engineering. The role operates in a matrix of industry practitioners, technologists, and alliance partners and requires strong collaboration, structured problem-solving, and the ability to translate emerging technology into operational results.

Work you'll do

You will serve as the functional and domain expert for Physical AI-where AI meets the physical world-across manufacturing and supply chain operations. You will shape advisory engagements, lead proofs of concept (PoCs), and drive implementation programs that combine robotics, computer vision, simulation, digital twins, synthetic data, and edge AI, frequently in partnership with ecosystem alliance providers (e.g., NVIDIA, Siemens, AWS and others). Candidates should be comfortable in factories, warehouses, and leadership conference rooms with the experience to translate between controls engineers, data scientists, and frontline operations.

Key responsibilities
  • Lead Physical AI strategy and advisory for manufacturing and supply chain clients. Identify high-value use cases (e.g., quality inspection, safety, intralogistics, material handling, asset monitoring, autonomous operations), define value hypotheses, and translate to roadmaps and business cases.
  • Own solution shaping and end-to-end architecture spanning sensors, vision, data pipelines, model development, simulation, edge deployment, and operations (i.e., MLOps and ModelOps), with explicit acceptance criteria for operational environments.
  • Drive PoCs and pilots to measurable outcomes. Define experiments, data collection plans, synthetic data approaches (when appropriate), evaluation metrics, and scale plans from pilot-to-plant and factory/network rollout.
  • Integrate AI with real-world constraints: latency, reliability, safety, OT/IT connectivity, cybersecurity, model drift, human-in-the-loop workflows, and maintenance/operating model considerations.
  • Partner with alliances and product teams to translate partner platforms into client-ready reference architectures, demos, and repeatable delivery assets.
  • Influence pursuits and proposals: support scoping, estimating, staffing, risk and assumption framing, and executive-level storytelling. Serve as technical authority in client workshops and due diligence.
  • Lead and mentor multi-disciplinary teams: data science, ML engineering, software and edge, vision, robotics and controls, manufacturing experts, and contribute to capability-building and market activation.

The team

Qualifications

Required

  • Bachelor's degree or equivalent practical experience.
  • 10+ years of relevant experience, including client leadership, team leadership, and sustained contribution to business development/pursuits.
  • Experience in at least two of the following domains: 
    • Computer vision for industrial environments (e.g., inspection, defect detection, safety, tracking, manufacturing assembly)
    • Robotics and autonomy (e.g., industrial robotics, mobile robotics and AMRs, perception-to-action workflows)
    • Simulation, digital twins, physics-based modeling for factories, lines, cells, warehousing, and logistics (e.g., discrete event, physics, MILP)
    • Synthetic data generation and validation approaches for model development
    • Edge AI deployment (e.g., performance, reliability, lifecycle operations)
  • Manufacturing and supply chain domain experience in areas such as discrete or process manufacturing, quality systems, maintenance and reliability, intralogistics, warehouse operations, plant OT/IT constraints, safety, and compliance.
  • Ability to travel up to 50%, based on the work you do and the clients and industries/sectors you serve
  • Must be legally authorized to work in the United States without the need for employer sponsorship, now or at any time in the future.

Preferred

  • Experience collaborating with technology partners (e.g., NVIDIA, Siemens, AWS. and/or similar ecosystems) to translate platforms into deliverable architectures and programs.
  • Manufacturing and supply chain technology exposure in areas such as sensing and IIoT connectivity, process and product optimization, automation and process control, fleet operations, machine learning and data science, cybersecurity for OT
  • Demonstrated experience leading client-facing advisory, PoCs, and implementations (not just research), including requirements, acceptance criteria, and operational handover.
  • Graduate degree (MS/PhD) in Robotics, Computer Science, Electrical/Mechanical Engineering, Industrial Engineering, Applied Physics, Operations Research, or related field.
  • Hands-on experience with NVIDIA ecosystem elements relevant to Physical AI (e.g., accelerated computing for vision/AI at the edge, simulation workflows, robotics stacks) and Siemens engineering platforms and tooling; ability to compare and compose with other vendor stacks.
  • Experience designing governance and operating models for Physical AI in production: model monitoring and drift, incident response, data management, human-in-the-loop, safety and controls integration.
  • Demonstrated thought leadership: reusable accelerators, reference architectures, demo assets, publications, or enablement delivered to internal/external audiences.
  • Business development contribution (pipeline creation, proposal leadership, account expansion) and executive stakeholder management.

The wage range for this role takes into account the wide range of factors that are considered in making compensation decisions including but not limited to skill sets; experience and training; licensure and certifications; and other business and organizational needs. The disclosed range estimate has not been adjusted for the applicable geographic differential associated with the location at which the position may be filled. At Deloitte, it is not typical for an individual to be hired at or near the top of the range for their role and compensation decisions are dependent on the facts and circumstances of each case. A reasonable estimate of the current range is $171,600 - $322,900.

You may also be eligible to participate in a discretionary annual incentive program, subject to the rules governing the program, whereby an award, if any, depends on various factors, including, without limitation, individual and organizational performance.

Information for applicants with a need for accommodation:https://www2.deloitte.com/us/en/pages/careers/articles/join-deloitte-assistance-for-disabled-applicants.html

SCNOFY27

#EPCORE

Qualifications:

Physical AI Senior Manager - Manufacturing & Supply Chain

We are a team of strategic advisors, architects, and implementers who drive business transformations. Our diverse talent energizes clients' business functions and technology to maximize value in Supply Chain enhancing their ability to fulfill their growth and efficiency ambitions. Imagine working with world-class supply network capabilities like Smart Factory, Strategy & Innovation, Supply Chain Responsiveness, Sourcing & Procurement, or Product Development & Operations!
Are you ready to take your career to new heights? Join our US Supply Chain & Network Operations Offering, where you'll deliver transformational solutions using operational expertise, digital technologies, advanced analytics, and industry-specific hybrid solutions. Don't miss the chance to be part of a team that provides exceptional client value while advancing your professional journey. Apply now and become a vital part of our innovative and dynamic workforce!

Recruiting for this role ends on 8/31/26.

The team

You will join a cross-functional Supply Chain & Manufacturing consulting environment focused on helping clients modernize operations through technology, data, and advanced engineering. The role operates in a matrix of industry practitioners, technologists, and alliance partners and requires strong collaboration, structured problem-solving, and the ability to translate emerging technology into operational results.

Work you'll do

You will serve as the functional and domain expert for Physical AI-where AI meets the physical world-across manufacturing and supply chain operations. You will shape advisory engagements, lead proofs of concept (PoCs), and drive implementation programs that combine robotics, computer vision, simulation, digital twins, synthetic data, and edge AI, frequently in partnership with ecosystem alliance providers (e.g., NVIDIA, Siemens, AWS and others). Candidates should be comfortable in factories, warehouses, and leadership conference rooms with the experience to translate between controls engineers, data scientists, and frontline operations.

Key responsibilities
  • Lead Physical AI strategy and advisory for manufacturing and supply chain clients. Identify high-value use cases (e.g., quality inspection, safety, intralogistics, material handling, asset monitoring, autonomous operations), define value hypotheses, and translate to roadmaps and business cases.
  • Own solution shaping and end-to-end architecture spanning sensors, vision, data pipelines, model development, simulation, edge deployment, and operations (i.e., MLOps and ModelOps), with explicit acceptance criteria for operational environments.
  • Drive PoCs and pilots to measurable outcomes. Define experiments, data collection plans, synthetic data approaches (when appropriate), evaluation metrics, and scale plans from pilot-to-plant and factory/network rollout.
  • Integrate AI with real-world constraints: latency, reliability, safety, OT/IT connectivity, cybersecurity, model drift, human-in-the-loop workflows, and maintenance/operating model considerations.
  • Partner with alliances and product teams to translate partner platforms into client-ready reference architectures, demos, and repeatable delivery assets.
  • Influence pursuits and proposals: support scoping, estimating, staffing, risk and assumption framing, and executive-level storytelling. Serve as technical authority in client workshops and due diligence.
  • Lead and mentor multi-disciplinary teams: data science, ML engineering, software and edge, vision, robotics and controls, manufacturing experts, and contribute to capability-building and market activation.

The team

Qualifications

Required

  • Bachelor's degree or equivalent practical experience.
  • 10+ years of relevant experience, including client leadership, team leadership, and sustained contribution to business development/pursuits.
  • Experience in at least two of the following domains: 
    • Computer vision for industrial environments (e.g., inspection, defect detection, safety, tracking, manufacturing assembly)
    • Robotics and autonomy (e.g., industrial robotics, mobile robotics and AMRs, perception-to-action workflows)
    • Simulation, digital twins, physics-based modeling for factories, lines, cells, warehousing, and logistics (e.g., discrete event, physics, MILP)
    • Synthetic data generation and validation approaches for model development
    • Edge AI deployment (e.g., performance, reliability, lifecycle operations)
  • Manufacturing and supply chain domain experience in areas such as discrete or process manufacturing, quality systems, maintenance and reliability, intralogistics, warehouse operations, plant OT/IT constraints, safety, and compliance.
  • Ability to travel up to 50%, based on the work you do and the clients and industries/sectors you serve
  • Must be legally authorized to work in the United States without the need for employer sponsorship, now or at any time in the future.

Preferred

  • Experience collaborating with technology partners (e.g., NVIDIA, Siemens, AWS. and/or similar ecosystems) to translate platforms into deliverable architectures and programs.
  • Manufacturing and supply chain technology exposure in areas such as sensing and IIoT connectivity, process and product optimization, automation and process control, fleet operations, machine learning and data science, cybersecurity for OT
  • Demonstrated experience leading client-facing advisory, PoCs, and implementations (not just research), including requirements, acceptance criteria, and operational handover.
  • Graduate degree (MS/PhD) in Robotics, Computer Science, Electrical/Mechanical Engineering, Industrial Engineering, Applied Physics, Operations Research, or related field.
  • Hands-on experience with NVIDIA ecosystem elements relevant to Physical AI (e.g., accelerated computing for vision/AI at the edge, simulation workflows, robotics stacks) and Siemens engineering platforms and tooling; ability to compare and compose with other vendor stacks.
  • Experience designing governance and operating models for Physical AI in production: model monitoring and drift, incident response, data management, human-in-the-loop, safety and controls integration.
  • Demonstrated thought leadership: reusable accelerators, reference architectures, demo assets, publications, or enablement delivered to internal/external audiences....

What Deloitte employees say

Pay

Benefits

Hours and flexibility

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