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Privacy Preserving Machine Learning Jobs in Sparta, NJ

Cyber AI Security Manager

Livingston, NJ · On-site +1

$121K - $163K/yr

Experience with data anonymization techniques and privacy-preserving machine learning methodologies. * Understanding of DevSecOps principles and experience integrating security into CI/CD pipelines.

Senior Application Developer (C++)

Ramsey, NJ · On-site

$95K - $131K/yr

... and self-learning. What we like to see: • Experience with Glib, GObject, GTK+, MinGW. • ... We respect your Online Privacy. This e-mail message, including any attachments, is for the sole use ...

C++ Developer

Ramsey, NJ · On-site

$48.75 - $65.75/hr

... and self-learning. What we like to see: • Experience with Glib, GObject, GTK+, MinGW. • ... We respect your Online Privacy. This e-mail message, including any attachments, is for the sole use ...

Showing results 41-47

Privacy Preserving Machine Learning information

See Sparta, NJ salary details

$104.7K

$121.5K

$136.2K

How much do privacy preserving machine learning jobs pay per year?

As of Sep 3, 2026, the average yearly pay for privacy preserving machine learning in Sparta, NJ is $121,497.00, according to ZipRecruiter salary data. Most workers in this role earn between $106,200.00 and $135,700.00 per year, depending on experience, location, and employer.

What is privacy preserving machine learning?

Privacy preserving machine learning refers to techniques and methods that allow data analysis and model training while protecting sensitive information. This field focuses on ensuring that personal or confidential data is not exposed or compromised during the development and deployment of machine learning models. Approaches such as federated learning, differential privacy, and homomorphic encryption are commonly used. These methods enable organizations to leverage data for insights and predictions without violating privacy regulations or risking data breaches. Privacy preserving machine learning is especially important in industries like healthcare, finance, and any sector handling personal data.

What are the key skills and qualifications needed to thrive as a privacy preserving machine learning engineer?

To thrive as a Privacy Preserving Machine Learning Engineer, you need a strong background in machine learning, data privacy techniques (such as differential privacy or federated learning), and a relevant degree in computer science or a related field. Familiarity with frameworks like TensorFlow Privacy, PySyft, and privacy-enhancing technologies, along with certifications in data security or privacy, are often required. Strong problem-solving abilities, meticulous attention to detail, and the ability to communicate complex technical concepts clearly set top professionals apart. These skills ensure the development of robust machine learning models that protect sensitive data while delivering valuable insights, maintaining compliance and trust.

What are some common challenges faced by professionals working in privacy preserving machine learning roles?

Professionals in Privacy Preserving Machine Learning often encounter challenges such as balancing model accuracy with strict privacy requirements, selecting appropriate privacy-preserving techniques (like differential privacy or federated learning), and ensuring compliance with evolving data protection regulations. Collaborative projects may also involve coordinating with legal, data security, and software engineering teams to implement robust solutions. Additionally, staying updated with the latest research and adapting to new threats or vulnerabilities is a continuous part of the role.

What is the difference between Privacy Preserving Machine Learning vs Data Scientist?

AspectPrivacy Preserving Machine LearningData Scientist
Required CredentialsTypically requires knowledge of machine learning, data privacy, and security certificationsRequires degrees in data science, statistics, or related fields; certifications like Certified Data Scientist are common
Work EnvironmentWorks in research, development, and implementation of privacy-focused ML models, often in tech or finance sectorsAnalyzes data, builds models, and provides insights across various industries including marketing, finance, and healthcare
Employer & Industry UsageUsed by organizations prioritizing data privacy, such as healthcare, finance, and tech companiesEmployed across diverse sectors for data analysis, predictive modeling, and decision support

Privacy Preserving Machine Learning focuses on developing models that protect data privacy during training and inference, while Data Scientists analyze and interpret data to generate insights. Both roles require strong analytical skills, but Privacy Preserving Machine Learning emphasizes security and privacy techniques, whereas Data Scientists focus on data analysis and modeling.

What cities near Sparta, NJ are hiring for Privacy Preserving Machine Learning jobs?

Cities near Sparta, NJ with the most Privacy Preserving Machine Learning job openings:

Infographic showing various Privacy Preserving Machine Learning job openings in Sparta, NJ as of August 2026, with employment types broken down into 1% As Needed, 72% Full Time, 23% Part Time, 1% Temporary, and 3% Contract. Highlights an 89% Physical, 2% Hybrid, and 9% Remote job distribution, with an average salary of $121,497 per year, or $58.4 per hour.

Associate Director/Director - BI Consulting & Client Advisory (1286)

Axtria, Inc

Berkeley Heights, NJ • On-site

$131 - $175/hr

Other

Posted 28 days ago


Job description

Introduction

Axtria is a leading global provider of cloud software and data analytics to the Life Sciences industry. We help Life Sciences companies transform the product commercialization journey to drive sales growth and improve healthcare outcomes for patients. We are acutely aware that our work impacts millions of patients and lead passionately to improve their lives.

Since our founding in 2010, innovation has been our winning differentiation, and we continue to leapfrog competition with platforms that deploy Artificial Intelligence and Machine Learning. Our cloud-based platforms - Axtria DataMax™, Axtria InsightsIQ™, Axtria SalesIQ™, and Axtria MarketingIQ™ - enable clients to efficiently manage data, leverage data science to deliver insights for sales and marketing planning, and manage end-to-end commercial operations. We work with over 100 Life Sciences companies, many with multiple engagements globally across 75+ countries. We continue to win industry recognition for growth and are featured in some of the most aspirational lists – INC 5000, Deloitte FAST 500TM, NJBiz FAST 50, SmartCEO Future 50, Red Herring 100, and several other growth and technology awards.

Axtria is looking for exceptional talent to join our rapidly growing global team. People are our biggest perk! Axtria is recognized for its organizational culture in both US and India, including certification by the Great Place to Work® Institute. Our transparent and collaborative culture offers a chance to work with some of the brightest minds in the industry. Axtria Institute, our in-house university, offers the best training in the industry and an opportunity to learn in a structured environment. A customized career progression plan ensures every Axtrian is setup for success and able to do meaningful work in a fun environment. We want our legacy to be the leaders we produce for the industry. Will you be next?

Role Overview

BI and AI capabilities are evolving quickly, so this role requires staying ahead of the curve—Gen BI, Agentic BI, semantic layers, natural-language analytics, AI copilots, embedded analytics, and decision intelligence—and translating those trends into real client value, not just tracking them from the sidelines. We are seeking a US-based Senior BI Consultant who can operate as a trusted advisor to pharma and life sciences clients, helping them modernize BI landscapes, shape executive-ready analytics strategies, and translate complex data ecosystems into clear business decisions. The ideal candidate combines deep BI platform experience, pharma commercial and/or medical domain fluency, and executive presence to bring these trends to life through compelling client conversations and demos.

Key Responsibilities 1. Client Advisory & Executive Engagement
  • Lead strategic conversations with CIO, CDO, Commercial Operations, Medical Affairs, Sales Operations, Insights & Analytics, and business leadership stakeholders.
  • Translate business priorities into BI transformation roadmaps, platform strategies, operating models, and analytics adoption plans.
  • Communicate technical concepts in concise, executive-friendly language with clear linkage to business outcomes, adoption, speed-to-insight, and ROI.
  • Facilitate discovery workshops, solution design sessions, executive readouts, and client decision forums.
2. BI Strategy, Modernization & Architecture
  • Advise clients on modern BI architecture across their platform ecosystem (see Core Platforms above).
  • Guide dashboard rationalization, semantic model design, KPI standardization, governance, security, performance optimization, and self-service enablement.
  • Lead modernization conversations from legacy BI and static reporting toward governed, AI-ready, decision-centric analytics environments.
  • Partner with delivery teams to ensure proposed solutions are technically feasible, scalable, compliant, and aligned to enterprise data strategy.
3. Pharma Commercial & Medical Domain Thought Leadership
  • Bring strong working knowledge of pharma commercial analytics, including brand performance, field force effectiveness, HCP / HCO engagement, market access, omnichannel, patient services, sales operations, and launch analytics.
  • Bring exposure to Medical Affairs analytics such as MSL insights, KOL engagement, scientific intelligence, congress analytics, medical content performance, and medical-commercial governance boundaries.
  • Shape analytics narratives that reflect regulated-industry expectations, including privacy, consent, MLR awareness, auditability, data lineage, and governance-by-design.
  • Use domain context to make demos and presentations relevant to pharma leaders, not generic BI audiences.
4. Demos, Presales & Value Storytelling
  • Create and present compelling demos, POVs, POCs, solution walkthroughs, capability decks, and executive narratives tailored to client maturity and business priorities.
  • Support RFPs, RFIs, proposals, account expansion motions, and pursuit discussions with differentiated BI and AI-enabled analytics messaging.
  • Convert technical features into value stories around business impact, user adoption, decision velocity, operating efficiency, and reduced analytics friction.
  • Lead conversations on how BI tools fit into evolving client environments, including coexistence with enterprise data platforms, cloud modernization, CRM, MDM, omnichannel, and AI ecosystems.
5. Gen BI, Agentic BI & Emerging Trends Leadership
  • Maintain a strong point of view on evolving BI trends such as Generative BI, Agentic BI, conversational analytics, AI copilots, semantic layers, knowledge graphs, embedded analytics, proactive alerts, and autonomous insight workflows.
  • Explain how modern BI is shifting from static dashboards to decision intelligence, with governed AI assisting users in moving from “what happened” to “why it happened” and “what should we do next.”
  • Guide clients on AI-readiness foundations, including trusted data models, KPI definitions, semantic governance, responsible AI guardrails, and human-in-the-loop validation.
  • Partner with product, AI, data engineering, and delivery teams to shape reusable accelerators, demos, and thought leadership assets.
6. Delivery Partnership & Capability Building
  • Provide direction to distributed teams of BI architects, consultants, developers, analysts, UX designers, data engineers, and AI specialists.
  • Review solution designs, dashboard concepts, demo flows, data models, and executive presentations for clarity, business relevance, and technical soundness.
  • Contribute to reusable consulting playbooks, solution patterns, discovery frameworks, KPI libraries, demo repositories, and client-ready accelerators.
  • Mentor team members on executive communication, solution storytelling, pharma context, and client advisory skills.
Required Qualifications & Skills
  • 12–15 years of experience in BI, analytics consulting, data visualization, enterprise reporting, solution advisory, or analytics transformation.
  • Strong hands‑on or architecture‑level expertise across the platforms listed in Core Platforms above (minimum two, preferably Power BI and Tableau), plus working knowledge of modern cloud and lakehouse / warehouse architectures.
  • Strong understanding of BI architecture: semantic layers, data modeling, dashboard performance, governance, row‑level security, KPI frameworks, data lineage, and self‑service analytics.
  • Strong pharma / life sciences domain experience in Commercial and/or Medical Affairs analytics.
  • Proven ability to lead executive presentations, client workshops, demos, and strategic advisory discussions, converting technical content into business value narratives for senior stakeholders.
  • Awareness of AI‑enabled analytics capabilities including Power BI Copilot, Tableau Pulse / Agent, ThoughtSpot AI, natural-language querying, Gen BI, Agentic BI, and knowledge graph / semantic‑layer approaches.
  • Excellent written and verbal communication, stakeholder management, facilitation, and consultative problem‑solving skills.
Preferred Qualifications
  • Prior consulting experience with US‑based pharma, biotech, or life sciences clients.
  • Experience in pre‑sales, solution shaping, RFP/RFI response development, account expansion, or GTM support.
  • Experience building or presenting demos related to executive dashboards, HCP 360, field insights, brand performance, market access, medical insights, or Gen BI / AI‑assisted analytics.
  • Experience with dashboard rationalization, BI platform migration, cloud modernization, KPI standardization, or BI operating model design.
  • Certifications in one or more of the core platforms listed above are preferred.
  • Familiarity with pharma data sources such as CRM, claims, sales, call activity, HCP / HCO master, formulary, market access, omnichannel, patient services, medical insights, or syndicated data.
Success Metrics
  • Client Impact: Quality of client relationships, stakeholder confidence, repeat conversations, and expansion opportunities.
  • Presales Contribution: Contribution to demos, proposals, POCs, RFPs/RFIs, qualified pipeline, and deal conversion.
  • Solution Quality: Strength of recommended BI architectures, modernization roadmaps, governance models, and AI‑readiness approaches.
  • Executive Communication: Ability to simplify complex BI and AI topics into concise, persuasive, leadership‑ready narratives.
  • Thought Leadership: Development of market‑relevant perspectives on Gen BI, Agentic BI, semantic layers, decision intelligence, and pharma analytics transformation.
  • Practice Enablement: Reusable assets, demo stories, playbooks, frameworks, and mentoring contributions that scale the BI CoE’s impact.
Ideal Candidate Profile

The ideal candidate is a senior, client‑facing BI leader who can walk into a pharma client conversation and confidently discuss business problems, BI platforms, AI‑enabled analytics trends, demo concepts, solution trade‑offs, and value realization. This person should be equally comfortable presenting to executives, whiteboarding solution approaches with architects, guiding delivery teams, and shaping a modern BI vision that moves clients from dashboards to decisions.

Pay Transparency Laws

Salary range or hourly pay range for the position

The salary range for this position is $131,200 to $174,975 annually. The actual salary will vary based on applicant’s education, experience, skills, and abilities, as well as internal equity and alignment with market data. The salary may also be adjusted based on applicant’s geographic location.

The salary range reflected is based on a primary work location of Berkeley Heights, New Jersey. The actual salary may vary for applicants in a different geographic location.

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