Interested in this AI/ML Engineer role at TRANZACT?
Apply Now →Skills & Technologies
About This Role
TRANZACT is a leading direct-to-consumer insurance services business, specializing in the distribution of Medicare Advantage, Medicare Supplement and Life & Supplemental insurance policies. Our focus is on leveraging our highly skilled team of over 3,000 professionals to deliver innovative solutions for our insurance carrier partners and provide consumers with a best-in-class experience.
We work with some of America’s largest insurance brands to attract, educate and connect with consumers. Our diverse team of analysts, writers, engineers, designers, business leads, data scientists and sales professionals all work together to create thoughtful, data-driven and effective marketing, technology, sales and servicing strategies. These efforts have resulted in TRANZACT earning recognition and winning awards from some of the biggest names in the industry.
When you become part of TRANZACT, you join a team of passionate people who push for excellence, follow through on commitments, and love to succeed together. Our culture rests on a simple principle – “Be Real” – that means being genuine, keeping your word, and having the courage to make tough decisions. Our “Be Real” values resonate and are meaningful to us. We are entrepreneurial, fast paced and empower our people. We know how to make things happen and know that our people are always the source of our success.
Position Summary
TRANZACT is looking for a hands-on data leader who thrives at the intersection of high-impact analytics, people development, and strategic influence to help shape the future of our data science organization by bringing deep technical mastery, a passion for mentorship, and a vision for what data can do when aligned with bold business goals.
As the Head of Data Science, you will oversee and support advanced analytics projects that impact the entire sales funnel and touch all of our product categories. You’ll drive delivery of a wide range of ML models and advanced analytics including predictive modeling, segmentation, lead scoring, marketing optimizations as well as crafting data stories and presentations of key findings to stakeholders.
Your primary role will be to put Data Science in the center of all our marketing and business decisions. To get there you need to have a deep understanding of data science marketing, what works, where to find leverage and how to build a team around driving best in class marketing outcomes. This is not a support role. While the marketing team may come to your team for help with certain analyses, your team’s primary role is to find areas of opportunity for marketing to execute against.
The Responsibilities of the Head of Data Science include:
- Help develop Data Science team’s project and roadmap in line with business objectives and strategic opportunities.
- Lead and oversee a team of (5) data scientists and analysts to deliver best-in-class impactful models and analysis.
- Day-to-day collaboration with cross-functional teams to understand evolving business needs and develop effective analytics solutions and models.
- Execute on a model strategy roadmap to continuously improve accuracy and functionality of models and business outcomes.
- Drive data science initiatives to improve customer engagement and operational efficiency through personalization and effective targeting.
- Deliver on varied projects such as customer audience segmentation, propensity models, and dynamic bidding algorithms.
- Be an SME and key producer of forecasting model development, production, and execution, in partnership with our Enterprise Data Management and Technology teams.
- Prepare and deliver presentations summarizing key insights and conclusions, often to senior leaders.
- Embrace and foster a curious growth and learning mindset for the entire team.
- Support a collaborative, team-oriented and client-focused organization that’s motivated by business and technical challenges.
Qualifications include:
- Bachelor's degree in a quantitative or related field, and 8+ years of relevant experience.
- Data Science experience within marketing. Needs to have done the following:
- Attribution modeling
- Audience segmentation, targeting and measurement. Experience within clean rooms a bonus
- Campaign lift analysis
- Predictive modeling
- KPI forecasting and scenario planning
- Funnel conversion analysis
- Geo performance and market opportunity analysis
- Marketing experimentation frameworks
- Hybrid work schedule in Fort Lee, NJ
- Strong leadership skills and successful outcomes as a people manager.
- Excellent SQL, Python, and Databricks skills.
- Intuitive business and analytical skills with the ability to collect, organize, analyze, and disseminate relevant information with attention to detail and accuracy to non-technical and senior leadership audiences.
- Deep experience with successful statistical modeling, machine learning and other advanced analytics methods.
- Proactive, results-driven, and have a proven track record of execution.
- Structured thinker with exceptional verbal and written communication skills.
Preferred qualifications include:
- Masters in a quantitative or related field (i.e., Data Science, Statistics, etc.).
- Experience in a direct to consumer, financial service, or insurance sector(s).
Compensation and Benefits
Base salary range and benefits information for this position are being included in accordance with requirements of various state/local pay transparency legislation. Please note that base salaries may vary for different individuals in the same role based on several factors, including but not limited to location of the role, individual competencies, education/professional certifications, qualifications/experience, performance in the role and potential for revenue generation.
Compensation
The base salary compensation range being offered for this role is $200,000 and above per year.
This role is also eligible to participate in the annual bonus program.
Company Benefits
TRANZACT provides a competitive benefit package which includes the following (eligibility requirements apply):
- Health and Welfare Benefits: Medical (including prescription coverage), Dental, Vision, Health Savings Account, Health Care and Dependent Care Flexible Spending Accounts, Group Accident, Group Critical Illness, Life Insurance, AD&D, Group Legal, Identity Theft Protection, Wellbeing Program and Work/Life Resources (including Employee Assistance Program).
- Leave Benefits: Paid Holidays, Annual Paid Time Off (includes state/local paid leave where required), Short-Term Disability, Long-Term Disability, Other Leaves (e.g. Bereavement, FMLA, ADA, Jury Duty, Military Leave, and Parental and Adoption Leave).
- Retirement Benefits: Savings Plan with annual nonelective company contribution.
TRANZACT is an Equal Opportunity Employer. We celebrate diversity and are committed to creating an inclusive environment for all employees.
Equal Opportunity Employer/Disability/Vet
Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 8,943 AI roles we're tracking, AI/ML Engineer positions make up 75% of the market. At TRANZACT, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills Required
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $212,000 based on 11,866 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200.
Across all AI roles, the market median is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,250; VP: $235,000.
TRANZACT AI Hiring
TRANZACT has 2 open AI roles right now. They're hiring across AI/ML Engineer. Positions span Fort Lee, NJ, US, Fort Lee, NJ.
Location Context
Across all AI roles, 6% (521 positions) offer remote work, while 8,351 require on-site attendance. Top AI hiring metros: New York (2,878 roles, $221,000 median); San Francisco (2,416 roles, $263,811 median); Los Angeles (1,340 roles, $210,000 median).
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 8,943 open positions tracked in our dataset. By seniority: 175 entry-level, 4,132 mid-level, 3,268 senior, and 1,368 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (521 positions). The remaining 8,351 roles require on-site or hybrid attendance.
The market median for AI roles is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 8,943 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,739), AI Product Manager (403), AI Software Engineer (402). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (175) are outnumbered by mid-level (4,132) and senior (3,268) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 1,368 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (521 positions), with 8,351 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $213,380. Top-quartile roles start at $264,148, and the 90th percentile reaches $319,310. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $253,100 median, while Prompt Engineer roles sit at $141,440. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (1,422 postings), Aws (808 postings), Azure (638 postings), Rag (555 postings), Gcp (457 postings), Pytorch (433 postings), Claude (341 postings), Kubernetes (337 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
Get Weekly AI Career Intelligence
Salary data, skills demand, and market signals from 16,000+ AI job postings. Every Monday.