Impress employers and recruiters.
Choose from hundreds of resume examples.

Target Resume Now

Tailor your resume to this Director, Data Science and Machine Learning (Risk and Fraud) role.

Rezi rewrites your resume against Trustly's job description. Free.

Resume score gauge reading 58 out of 100

Don't guess if your resume is good enough.

See how it scores against the Director, Data Science and Machine Learning (Risk and Fraud) posting at Trustly — free, in seconds.

About the Role

You will lead the team responsible for building and deploying machine learning models that determine transaction approval and guarantee. This hands-on technical leadership role involves building and shipping models yourself while leading, mentoring, and developing a team of Data Scientists and Machine Learning Engineers. You will own the team's roadmap, quality, performance, and business impact in production, working at the intersection of machine learning, payments risk, product, and engineering.

Responsibilities

Build, validate, deploy, and continuously improve machine learning models across ACH return risk, fraud, and account takeover.
Develop real-time, online, and offline features using transaction, account, device, and behavioral data.
Design and analyze experiments to evaluate model and policy changes, including champion/challenger tests, holdouts, and staged rollouts.
Quantify the impact of model changes across loss rates, approval rates, ROI, customer experience, and merchant outcomes.
Translate complex model performance and findings into clear, actionable insights for non-technical partners across Risk, Product, Finance, and other areas of the business.
Mentor, develop, and grow a team of full-stack Data Scientists and Machine Learning Engineers.
Hire and level talent with the right balance of modeling depth, experimentation expertise, and production engineering capability.
Own the team's delivery and outcomes: what ships, when it ships, how it performs, and whether it holds up in production.
Own the model roadmap across ACH return risk, fraud, and account takeover, including prioritization, model reviews, and postmortems following loss events.
Own model performance in production, including monitoring, drift detection, retraining cadence, model updates, and decisions around when models should be replaced or retired.
Establish a high bar for technical quality, operational rigor, and measurable business impact across the team.
Maintain strong model documentation, validation evidence, and audit trails, and support independent validation of the team's models.
Ensure production decisions are explainable, with appropriate reason codes available when decisions need to be justified to merchants, internal reviewers, or auditors.
Partner closely with Product and Engineering on scoring latency, feature availability at decision time, and integration into Trustly's risk engine.
Co-own decision strategy and the policy layer with Risk Operations, translating model outputs into business outcomes.
Quantify and communicate the tradeoffs between approval rates, customer experience, and loss rates when recommending changes.
Co-own loss forecasting with Finance and Risk, connecting model performance and behavior directly to financial outcomes.
Present model performance, loss drivers, emerging risks, and roadmap tradeoffs to senior and executive stakeholders.

Requirements

8+ years of experience building and deploying machine learning models into production, including at least 2 years leading a team.
A leader who remains deeply hands-on and is currently capable of independently building, deploying, and evaluating a production machine learning model.
Strong Python and SQL skills, with experience working with large-scale datasets using distributed processing engines or cloud data warehouses such as Athena, Trino, Spark, Redshift, Snowflake, or BigQuery.
Direct experience in fraud, credit risk, payments risk, or another high-stakes decisioning environment, including rare-event modeling, label delay, reject inference, and understanding the difference between strong model metrics and strong business decisions.
Experience with real-time inference and production model monitoring in a low-latency decisioning environment, including drift detection, retraining strategies, and model updates.
Experience owning or materially contributing to a loss budget or loss forecast, with the ability to translate model behavior and risk performance into financial impact for executives and auditors.
Strong experimentation experience for model and policy changes, including champion/challenger testing, holdouts, staged rollouts, and rigorous measurement of outcomes.
Demonstrated experience mentoring and developing Data Scientists and Machine Learning Engineers.

Skills

Python
SQL
pandas
scikit-learn
XGBoost
LightGBM
CatBoost
ACH or EFT domain expertise
NACHA return codes
open banking
bank transaction data
Databricks
AWS SageMaker
GCP Vertex AI
feature stores
streaming feature computation
rules engines
real-time decisioning systems
sequence modeling
graph-based modeling
recurrent architectures
entity-link analysis
model risk management
governance
third-party risk data
model providers

Location

San Francisco
New York

Work Type

Hybrid

Experience Level

Director
Lead

Education Level

Bachelor's degree in a quantitative discipline such as Statistics, Mathematics, Computer Science, Economics, Engineering, or Operations Research.

Salary/Compensations

The range displayed on each job posting reflects the minimum and maximum target for new hire salaries for the position across all US locations. Within the range, individual pay is determined by work location and additional factors, including job-related skills, experience, and relevant education or training. Recruiters can share more information with applicants about the specific salary range for preferred locations during the hiring process. Please note that the compensation details listed in US role postings reflect the base salary only, and do not include other perks and benefits.

Benefits

Flexible paid time off & generous PTO accrual plans
Comprehensive medical, dental, vision, and other insurances
FSA & HSA plans for medical and dependent care
Home office set-up allowance
Internet stipend
Retirement plan match for 401k and RRSP
Gender-neutral paid parental leave

About the Company

At Trustly, we're building a smarter, faster, and more secure financial future by revolutionizing the world of payments. As a global leader in Open Banking Payments, we are establishing Pay by Bank as the new standard at checkout, providing unparalleled freedom, speed, and ease to millions of consumers and merchants worldwide.
Our Ambition: To build the world’s most disruptive payment network and redefine what the payment experience should feel like.
Trustly is a global team of innovators, collaborators, and doers. If you are driven by a strong sense of purpose and thrive in a dynamic, entrepreneurial, and high-growth environment, join us and be part of a team that’s transforming the way the world pays.
The Data team at Trustly is the backbone of our decision-making and product innovation. We are a global, multidisciplinary collective of Software Engineers, Data Engineers, Machine Learning Specialists, Scientists, and Analysts who believe that data is more than just rows and columns - it is the fuel for a more transparent financial ecosystem.
We operate at the intersection of high-scale engineering and deep financial intelligence. Our team processes millions of transactions in real time, turning complex Open Banking signals into seamless payment experiences. We pride ourselves on a culture of intellectual curiosity, technical excellence, and extreme ownership.

Equal Opportunity

At Trustly, we embrace and celebrate diversity of all forms and the value it brings to our employees and customers. We are proud and committed to being an Equal Opportunity Employer and believe an open and inclusive environment enables people to do their best work. All decisions regarding hiring, advancement, and any other aspects of employment are made solely on the basis of qualifications, merit, and business need.