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Fraud Detection Machine Learning Jobs in Norwalk, CT

Arlo

Machine Learning Engineer

New York, NY · On-site

$180K - $230K/yr

It's the machinery wrapped around it: middlemen taking a cut, fraud nobody stops, and billing ... Arlo's underwriting is the core of the business, and it runs on machine learning at serious scale.

Arlo

Machine Learning Engineer

New York, NY · On-site

$180K - $230K/yr

It's the machinery wrapped around it: middlemen taking a cut, fraud nobody stops, and billing ... Arlo's underwriting is the core of the business, and it runs on machine learning at serious scale.

BioCatch

Pre-Sales Consultant

New York, NY · On-site +1

... and machine learning in fraud detection. Ability to explain technical concepts to non-technical audiences. • Demonstrate expert sales acumen at the enterprise level, including outstanding ...

Crowe LLP

Machine Learning Intern

New York, NY · On-site

$27 - $42/hr

Anomaly detection using deep neural networks Numerical optimization applied to problems in manufacturing Personal identifiable information (PII) and personal health information (PHI) detection in ...

WireScreen

Machine Learning Engineer

New York, NY · On-site

$150K - $195K/yr

... detection * End-to-end machine learning model experience in production; that you've stood up a service including experimenting, training, testing and tuning a job against a dataset all the way ...

Charlie Health

Staff Machine Learning Engineer

New York, NY · Remote

$175K - $255K/yr

Design, train, and evaluate machine learning models and AI systems that drive meaningful business ... Please be cautious of potential recruitment fraud. If you are interested in exploring opportunities ...

Current

Senior Software Engineer, Applied ML

New York, NY · On-site

$200K - $250K/yr

We work across a broad set of domains, including user-facing products like liquidity offerings and reward programs, infrastructure for machine learning and experimentation, real-time fraud detection ...

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Fraud Detection Machine Learning information

See Norwalk, CT salary details

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How much do fraud detection machine learning jobs pay per hour?

As of Sep 5, 2026, the average hourly pay for fraud detection machine learning in Norwalk, CT is $18.12, according to ZipRecruiter salary data. Most workers in this role earn between $14.95 and $19.33 per hour, depending on experience, location, and employer.

What is fraud detection using machine learning?

Fraud detection using machine learning involves leveraging algorithms and data analysis techniques to identify suspicious or fraudulent activities in various domains, such as banking, e-commerce, or insurance. These systems analyze large volumes of transaction data to detect patterns or anomalies that may indicate fraud. Machine learning models can adapt over time, improving their accuracy as they are exposed to more data. This approach helps organizations automate and enhance their ability to prevent, detect, and respond to fraudulent behavior efficiently.

What are some common challenges faced by professionals working in fraud detection machine learning, and how can they be addressed?

Professionals in Fraud Detection Machine Learning often face challenges such as dealing with highly imbalanced datasets, rapidly evolving fraud patterns, and the need for real-time detection. Managing data imbalance requires careful selection of evaluation metrics and specialized algorithms. Staying ahead of new fraud tactics involves continuous model retraining and close collaboration with domain experts. Additionally, integrating machine learning solutions with existing systems often requires cross-functional teamwork with IT, security, and compliance teams.

What are the key skills and qualifications needed to thrive as a fraud detection machine learning specialist, and why are they important?

To thrive as a Fraud Detection Machine Learning Specialist, you need strong expertise in machine learning, statistical analysis, and programming languages like Python or R, typically supported by a degree in computer science, data science, or a related field. Familiarity with tools such as TensorFlow, Scikit-learn, SQL databases, and experience with big data platforms or cloud services is highly valuable. Critical thinking, attention to detail, and effective communication are crucial soft skills for identifying complex fraud patterns and collaborating with interdisciplinary teams. These competencies are vital for developing accurate models that protect organizations from financial losses and maintain trust with customers.

What is the difference between Fraud Detection Machine Learning vs Fraud Analyst?

AspectFraud Detection Machine LearningFraud Analyst
CredentialsData science, machine learning certifications, programming skillsFinance, criminal justice degrees, analytical skills
Work EnvironmentData-driven, tech-focused, often in financial or e-commerce sectorsInvestigative, report-focused, in financial institutions or insurance companies
Employer & IndustryTech companies, banks, e-commerce platformsFinancial institutions, insurance firms, retail

Fraud Detection Machine Learning involves developing algorithms to identify fraudulent activities automatically, relying heavily on data analysis and programming. Fraud Analysts manually investigate suspicious cases and interpret data insights. While both roles aim to prevent fraud, Machine Learning specialists focus on building models, whereas Fraud Analysts focus on case investigation and decision-making.

What cities near Norwalk, CT are hiring for Fraud Detection Machine Learning jobs?

Cities near Norwalk, CT with the most Fraud Detection Machine Learning job openings:

Fraud Detection Machine Learning jobs near you

Machine Learning Engineer

Arlo

New York, NY • On-site

$180K - $230K/yr

Full-time

Re-posted 8 days ago

Job description

Most of what makes American healthcare expensive isn't medical care. It's the machinery wrapped around it: middlemen taking a cut, fraud nobody stops, and billing systems designed to fight over payment instead of deliver care. The result is higher premiums, denied claims, surprise bills, and a system patients increasingly experience as adversarial.
Arlo is rebuilding health insurance for small businesses from first principles: making sure as much of every premium dollar as possible goes to care instead of getting absorbed by the system around it. We do that by identifying fraud earlier, steering members toward higher-quality and lower-cost care, automating operational overhead, and eliminating vendors whose business exists mostly to take a cut.
AI is the foundation that makes this work. We use it across underwriting, operations, clinical programs, and member experience to build an insurer that becomes more efficient as the technology improves.
We're already operating at meaningful scale: profitable, hundreds of millions in premiums, tens of thousands of members covered, and growing quickly through brokers, employers, and partners. Backed by Upfront Ventures, 8VC, and General Catalyst, with a team from Palantir, YC companies, and longtime healthcare operators.
Arlo's underwriting is the core of the business, and it runs on machine learning at serious scale. We're hiring ML Engineer to build and own the infrastructure that powers it - from training models on tens of millions of patients and hundreds of millions of rows of claims data, to serving real-time quotes in seconds against inference-time datasets that run into the trillions of rows. You'll also build the tooling that lets our data scientists and actuaries iterate faster than ever.
This is an ML infrastructure role with real room to do ML and data science. You'll own the platform, but you'll also have the opportunity to work alongside our data scientists and actuaries to test and evaluate your own ideas - not just support theirs.
What You'll Work On
Training infrastructure for underwriting
  • Build and own the infrastructure layer that powers our underwriting model, trained on tens of millions of patients and hundreds of millions of rows of claims data.
  • Make training reliable, reproducible, and scalable as data volume and model complexity grow.

Real-time inference for quoting
  • Build and own the API layer that produces quotes in seconds - serving a trained model against a much larger inference-time dataset, on the order of trillions of rows of claims across hundreds of millions of people.
  • Own the latency, reliability, and scalability of the serving path the quoting product depends on.

Accelerate data science iteration
  • Make it as easy as possible for data scientists and actuaries to test new features and ideas.
  • Build backtesting and validation infrastructure so model performance can be measured quickly and trustworthily.
  • Remove friction from the path between an idea and a validated, production-ready model - make experimentation simpler than it's ever been.

What We're Looking For
  • A strong track record building ML or data infrastructure in production at scale.
  • Deep proficiency in Python, with comfort in processing large datasets (Spark, Databricks, or equivalent).
  • Experience with model training pipelines and/or low-latency model serving in production.
  • Experience building tooling that makes other people faster - feature testing, experiment tracking, backtesting, or similar developer/researcher-facing infrastructure.
  • The ability to own systems end-to-end, set standards, and operate reliable production infrastructure (SLAs, monitoring, on-call).
  • Genuine interest in the modeling itself - you want to occasionally get your hands into the data science, not only the infrastructure.

Nice to Have
  • Prior experience in a regulated space like healthcare or insurance.
  • Experience with MLOps tooling (MLflow or similar), feature stores, or experimentation platforms.
  • Experience supporting data science or actuarial teams in production environments.

Compensation
$180,000 - $230,000 + equity
Why Join Arlo:
  • High ownership: You'll get real responsibility from day one-our high-trust team empowers you to run with big problems and shape core parts of the company.
  • Join an important mission: Your work directly influences how people access care and improves lives at scale.
  • Growth & expansion: We're moving fast, and as we grow, your scope will grow with us-new challenges, bigger opportunities, and rapid career velocity.
  • Apply AI to a problem that matters: Instead of optimizing ads or cutting labor costs, you'll use AI to fundamentally reimagine how people get healthcare.
  • High pace, high collaboration: We operate with velocity, first-principles thinking, and a team that works closely, openly, and with ambition.

Exact compensation inclusive of salary and any bonuses is determined based on a number of factors including experience and skill level, location, and qualifications which are assessed during the interview process.
Arlo is an equal opportunity employer. We do not discriminate based on age, race, color, creed or religion, national origin, sexual orientation, gender identity or expression, military status, sex, disability, predisposing genetic characteristics, marital status, familial status, status as a victim of domestic violence, or arrest or conviction record, as defined under New York State law.
Your safety matters to us. If you're selected to move forward in our hiring process, you'll hear directly from a member of our Recruiting team via an @joinarlo.com email address. We will never ask for personal or financial information outside of our formal onboarding process. When in doubt, please reach out to us to verify at: recruiting@joinarlo.com.

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