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About This Role
About the Role
We are looking for an experienced Data Scientist / ML Engineer with a strong background in financial risk, fraud detection, and transaction monitoring to build next\-generation machine learning solutions. In this role, you will own the end\-to\-end lifecycle of risk models—from working with raw, high\-volume transaction data and unstructured financial text to designing, training, deploying, and auditing models in production.
You will work closely with Risk Operations, Compliance, and Data Engineering teams to build scalable models that directly reduce fraud losses, minimize false positives, and safeguard our operations.
Key Responsibilities
- Model Development: Design, build, validate, and deploy high\-performing machine learning models (supervised classification, anomaly detection, time\-series forecasting) for fraud detection, Anti\-Money Laundering (AML), and risk assessment.
- End\-to\-End Pipeline Ownership: Lead the full model lifecycle—from data acquisition and feature engineering on massive datasets to model training, A/B testing, backtesting, and production handoff.
- Unstructured Data \& AI: Leverage NLP and advanced techniques (LLMs, FinBERT, Document Intelligence) to parse unstructured financial reports, document formats, and sentiment signals into predictive features.
- False Positive Reduction \& Auditing: Perform rigorous error analysis, failure\-mode analysis, and segment auditing to continuously refine decision boundaries and optimize false positive vs. true positive tradeoffs.
- Data \& Feature Engineering: Build high\-throughput feature pipelines (rolling volatility, behavioral metrics, z\-scores) handling multi\-gigabyte/terabyte time\-series transaction streams.
- Cross\-Functional Leadership: Partner with Risk Ops and Compliance teams to define ground\-truth labeling standards, translate statistical outputs into actionable insights, and build interactive dashboards to track performance metrics.
Key Qualifications
- Experience: 3–5\+ years of experience as a Data Scientist or ML Engineer, specifically building and deploying models within Financial Services, Fintech, Payments, Risk, or Fraud domains.
- Core ML \& Stats Expertise: Strong proficiency in classification, clustering, anomaly detection (e.g., Isolation Forest, DBSCAN), time\-series analysis (ARIMA, Prophet, LSTM), and statistical testing.
- Unstructured Data / NLP: Demonstrated experience working with NLP frameworks (PyTorch, Hugging Face, LangChain) or Document AI to extract structured risk signals from text/documents.
- Programming \& Big Data: Advanced skills in Python (Scikit\-Learn, PyTorch/TensorFlow, XGBoost) and SQL. Hands\-on experience with PySpark / Databricks or similar big data tools for processing large\-scale datasets.
- Cloud \& MLOps: Experience operating in cloud environments (AWS S3, EC2, SageMaker, Redshift, or GCP) and using tools like Docker, MLflow, or FastAPI for operationalizing models.
- Domain Knowledge: Solid understanding of financial risk concepts (VaR, Monte Carlo simulations, portfolio risk metrics, or AML compliance) is a strong plus.
- Education: Master’s degree or Bachelor’s degree in Data Science, Computer Science, Statistics, Quantitative Finance, or a related field.
Pay: $150,000\.00 \- $175,000\.00 per year
Work Location: Remote
Salary Context
This $150K-$175K range is above the median for Data Scientist roles in our dataset (median: $160K across 258 roles with salary data).
View full Data Scientist salary data →Role Details
About This Role
Data Scientists extract insights and build predictive models from data. In the AI era, many roles now include LLM-powered analytics, automated reporting, and integration with generative AI tools. The role has evolved from 'the person who runs SQL queries' to 'the person who builds AI-powered data products.'
Modern data science roles fall into two camps: analytics-focused (insights, dashboards, experimentation) and ML-focused (building predictive models, recommendation systems, NLP features). The best data scientists can operate in both modes. The AI shift means that even analytics-focused roles now involve building automated insight pipelines using LLMs, going well beyond one-off reports.
Across the 4,317 AI roles we're tracking, Data Scientist positions make up 8% of the market. At HireBrick, this role fits into their broader AI and engineering organization.
Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.
What the Work Looks Like
A typical week includes: analyzing experiment results for a product feature launch, building a predictive model for customer churn, creating an automated reporting pipeline using LLM-powered summarization, presenting insights to stakeholders, and cleaning data (always cleaning data). The ratio of analysis to engineering varies by company, but expect both.
Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.
Skills Required
Python, SQL, and statistical modeling are the foundation. Increasingly, roles want experience with LLMs for data analysis, automated insight generation, and building AI-powered data products. Familiarity with cloud data platforms (Snowflake, BigQuery, Databricks) and ML frameworks (scikit-learn, PyTorch) covers most job requirements.
Experimentation design and causal inference are underrated skills that separate strong candidates. Companies care about whether their product changes cause improvements, and can distinguish causation from correlation. A/B testing methodology, Bayesian statistics, and the ability to communicate uncertainty to non-technical stakeholders are high-value skills.
Good postings specify the data stack, the types of problems you'll work on, and the team structure. Look for companies that differentiate between analytics and ML data science. Vague 'data scientist' postings that list every skill under the sun usually mean the company doesn't know what they need.
Compensation Benchmarks
Data Scientist roles pay a median of $192,890 based on 789 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($162K) sits 16% below the category median. Disclosed range: $150K to $175K.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
HireBrick AI Hiring
HireBrick has 2 open AI roles right now. They're hiring across AI/ML Engineer, Data Scientist. Based in Remote, US. Compensation range: $150K - $175K.
Remote Work Context
Remote AI roles pay a median of $180,000 across 1,196 positions. About 15% of all AI roles offer remote work.
Career Path
Common paths into Data Scientist roles include Data Analyst, Statistician, Quantitative Researcher.
From here, career progression typically leads toward Senior Data Scientist, ML Engineer, AI Product Manager.
Start with statistics and SQL. Build a real analysis project on public data that demonstrates insight generation alongside model building. The market values data scientists who can communicate findings clearly to business stakeholders. If you want to move toward ML engineering, invest in software engineering fundamentals and production deployment skills.
What to Expect in Interviews
Interviews combine statistics, coding, and business acumen. SQL is almost always tested, often with complex joins and window functions. Expect a case study round where you're given a business problem and asked to design an analysis plan. Coding rounds focus on pandas, statistical modeling, and visualization. The strongest differentiator is how well you communicate insights to non-technical stakeholders during presentation rounds.
When evaluating opportunities: Good postings specify the data stack, the types of problems you'll work on, and the team structure. Look for companies that differentiate between analytics and ML data science. Vague 'data scientist' postings that list every skill under the sun usually mean the company doesn't know what they need.
AI Hiring Overview
The AI job market has 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 roles).
Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.
The AI Job Market Today
The AI job market spans 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). 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 (138) are outnumbered by mid-level (2,071) and senior (1,655) 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 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 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 $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. 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 $287,500 median, while Prompt Engineer roles sit at $145,000. 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 (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 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.
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