> Markdown version of [/jobs/ext/2677325-customer-data-scientist](https://www.wearedevelopers.com/jobs/ext/2677325-customer-data-scientist). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Customer Data Scientist - **Company:** Hawk, Inc. - **Location:** United States (Remote available) - **Experience:** Starter - **Salary:** $150,000.0 - $225,000.0 - **Contract:** Permanent contract - **Skills:** Java (Programming Language), .NET Framework, Artificial Intelligence, Business Analytics Applications, Applications Architecture, Delphi (Programming Language), Cloud Computing, Fraud Prevention and Detection, Python (Programming Language), Machine Learning, Systems Development Life Cycle, SQL Databases, Digital Twin, Large Language Models, Spotfire - **Published:** September 2, 2026 - **Apply:** https://jobs.ashbyhq.com/hawk/19be8dc7-5b39-45e7-b0d0-9b2d9154ef9f ## About the Role * 5-7 years as a data scientist in a customer-facing role, presenting analysis and defending model decisions directly to clients. This is not an internal-facing engineering or product data science background; you've sat across the table from a customer before. * Real experience in AML, fraud detection, or financial crime analytics is required. You understand transaction monitoring, typologies, and what a false positive actually costs an investigator, not just what one costs on a confusion matrix. * Strong hands-on skills in the standard data science stack (Python, SQL, and whatever ML tooling you've used in production), but your edge is judgment under ambiguity: knowing when a model change is safe to make and when it needs a human in the loop. * Comfortable being the technical voice in a room with a customer's risk, compliance, or data science stakeholders, and holding your own when questioned. * A track record of translating model performance into business value a non-technical stakeholder can act on: not just accuracy metrics, but investigator hours saved, false positive cost avoided, and cases caught. * Genuine comfort with ambiguity and live production systems. You're not looking for a clean offline research problem, you're looking for the "why did this alert fire on a real customer's data at 2pm today" problem. * An ownership mentality. You don't wait for a ticket; you notice when an account's detection performance is drifting and you go find out why., * Familiarity with explainable AI and rules-based hybrid detection approaches, since that's core to how Hawk's models work. ## Description * Tune and optimize detection models and thresholds against each customer's live transaction data, balancing detection effectiveness against false positive load, not just against a benchmark dataset. * Investigate detection performance deeply: dig into missed cases, alert quality, and pattern drift, and turn what you find into concrete tuning or configuration changes. * Translate technical findings into customer-facing insight: build the analysis that shows investigator productivity gains, false positive cost reduction, and detection effectiveness improvements in language a customer's compliance and risk leadership actually uses. * Sit in the room with customers directly. Present findings, defend your methodology to a customer's own data science or compliance team, and answer the hard "why did the model do this" questions live. * Partner closely with your regional Customer Value Partners on account strategy, informing where the model needs to change to unlock the next stage of value realization or a renewal conversation. * Feed patterns and findings back into Hawk's broader model and product functions, distinguishing between "this customer needs local tuning" and "this is a systemic gap worth fixing centrally." * Own the regulatory defensibility of the tuning decisions you make. Document your reasoning so a customer's audit or regulator review holds up. * Bring rigor to how you validate model changes before they go live: backtesting, sample review, and sign-off discipline that protects the customer's compliance posture., * Experience specifically in transaction monitoring or payments fraud detection at a bank, payment provider, or a vendor serving them., Lead customer implementations of SOPHiA GENETICS genomic analysis solutions, from planning and sample selection through configuration, training, adoption, and issue resolution. Manage MaxCare Program schedules, timelines, sampling strategies, Statements of Work, technical setup, and cross-functional delivery. Translate laboratory, bioinformatics, data, and clinical regulatory requirements into practical solutions while building trusted customer relationships. The field-based US role includes approximately 30% travel. Top Skills: BioinformaticsCustom ReportingFederated Sso AuthenticationLibrary PreparationNext-Generation SequencingSophia Ddm Platform PNC Bank Software Engineer 2 Hours Ago Remote or Hybrid USA 75K-150K Annually Junior 75K-150K Annually Junior Machine Learning * Payments * Security * Software * Financial Services Develops, tests, deploys, maintains, and debugs software across the full project lifecycle. Translates business requirements into technical designs, supports production systems, documents solutions, estimates development tasks, collaborates with teammates, and mentors newer developers. The role requires application architecture, SDLC, testing, troubleshooting, and maintenance experience using Java, .NET, and/or Delphi, with Delphi preferred. Top Skills: .NetDelphiJava What you need to know about the Colorado Tech Scene With a business-friendly climate and research universities like CU Boulder and Colorado State, Colorado has made a name for itself as a startup ecosystem. The state boasts a skilled workforce and high quality of life thanks to its affordable housing, vibrant cultural scene and unparalleled opportunities for outdoor recreation. Colorado is also home to the National Renewable Energy Laboratory, helping cement its status as a hub for renewable energy innovation. Key Facts About Colorado Tech * Number of Tech Workers: 260,000; 8.5% of overall workforce (2024 CompTIA survey) * Major Tech Employers: Lockheed Martin, Century Link, Comcast, BAE Systems, Level 3 * Key Industries: Software, artificial intelligence, aerospace, e-commerce, fintech, healthtech * Funding Landscape: $4.9 billion in VC funding in 2024 (Pitchbook) * Notable Investors: Access Venture Partners, Ridgeline Ventures, Techstars, Blackhorn Ventures * Research Centers and Universities: Colorado School of Mines, University of Colorado Boulder, University of Denver, Colorado State University, Mesa Laboratory, Space Science Institute, National Center for Atmospheric Research, National Renewable Energy Laboratory, Gottlieb Institute ## Related Videos - [Fault Tolerance and Consistency at Scale: Harnessing the Power of Distributed SQL Databases](https://www.wearedevelopers.com/videos/1146-fault-tolerance-and-consistency-at-scale-harnessing-the-power-of-distributed-sql-databases) - [AI in Regulated Industry - Validating AI-Enabled Products with PLM and Digital Twins](https://www.wearedevelopers.com/videos/2065-ai-in-regulated-industry-validating-ai-enabled-products-with-plm-and-digital-twins) - [Data Science in Retail](https://www.wearedevelopers.com/videos/586-data-science-in-retail) - [Fault Tolerance and Consistency at Scale: Harnessing the Power of Distributed SQL Databases](https://www.wearedevelopers.com/videos/1520-fault-tolerance-and-consistency-at-scale-harnessing-the-power-of-distributed-sql-databases) - [Every CEO needs a digital twin to understand the scope of generative AI](https://www.wearedevelopers.com/videos/1006-every-ceo-needs-a-digital-twin-to-understand-the-scope-of-generative-ai) - [Anomaly Detection - Using unsupervised Machine Learning for detecting anomalies in customer base](https://www.wearedevelopers.com/videos/6-anomaly-detection-using-unsupervised-machine-learning-for-detecting-anomalies-in-customer-base) ## Related Articles - [Coffee with Developers - Maria Apazoglou - Making AI understandable for all in production](https://www.wearedevelopers.com/magazine/475-coffee-with-developers-maria-apazoglou-making-ai-understandable-for-all-in-production) - [Navigating the AI Shift](https://www.wearedevelopers.com/magazine/629-navigating-the-ai-shift) - [How to Become an AI Engineer](https://www.wearedevelopers.com/magazine/331-how-to-become-an-ai-engineer) - [How We Built a Worry-Free System That Runs for 10+ Years – And What We’d Do Again](https://www.wearedevelopers.com/magazine/751-how-we-built-a-worry-free-system-that-runs-for-10-years-and-what-we-d-do-again) - [Trustworthy AI Starts at Deployment: 5 Checks Before You Ship](https://www.wearedevelopers.com/magazine/753-trustworthy-ai-starts-at-deployment-5-checks-before-you-ship) - [9 Ways to Make Money Hacking](https://www.wearedevelopers.com/magazine/333-9-ways-to-make-money-hacking)