Job Description
About GeoComply
We’re GeoComply! We are at the forefront of geolocation, cybersecurity, and anti-fraud innovation, developing and delivering cutting-edge technologies to help ensure regulatory compliance, combat bad online actors, alleviate user friction, and protect businesses from fraud.
Achieving significant business and revenue growth over the past three years and dubbed a tech “Unicorn,” GeoComply has been trusted by leading global brands and regulators for over ten years. Our compliance-grade geolocation technology solutions are installed on over 400 million devices and analyze over 12 billion transactions a year.
At the heart of it all is the people, united by a deep commitment to problem-solving and revolutionizing how people and businesses use the internet to instill confidence in every online interaction. With teams across five countries, three continents, and a global customer base, we have no plans to slow down.
The Role
As a Senior Data Scientist, you will lead deep model research and rapid prototyping - identifying promising algorithms, crafting new features, and running rigorous experiments - while partnering with the Engineer and Operations teams to transition successful prototypes into production‑ready solutions. You will design and deploy machine learning models that cut false positives, uncover novel spoofing vectors, and scale seamlessly across multiple products.
Key Responsibilities
- Explore multi‑modal data, develop and implement advanced machine learning models to detect location spoofing patterns within large datasets.
- Build robust feature pipelines and monitoring dashboards that surface model performance drifts within minutes.
- Mine cross‑product ML signals to build composite risk scores and network‑level intelligence.
- Research advanced techniques (graph neural nets, time‑series anomaly detection, LLM, etc.) to stay ahead of emerging fraud patterns.
- Act as a leader in defining milestones, delegating tasks, and ensuring on‑time delivery.
- Coach and review the work of Data Scientists/Analysts; foster best practices in code review, experimentation, and documentation.
- Collaborate Cross-Functionally: Work closely with engineer managers, data engineers, and other stakeholders to refine requirements, integrate the model into the ML platform, and make a measurable impact.
- Translate model outputs into clear narratives and recommendations to the team through clear and concise reports and presentations.
- Document methodologies, processes, and best practices related to data science workflows, model development, and deployment for internal reference and regulatory compliance purposes.
- Other Tasks as Assigned: Flexibility is key in a dynamic environment; you’ll help out on various projects and initiatives as needed.
Who You Are
- You have over 5 years of experience in machine learning, including supervised and unsupervised learning techniques, deep learning, and anomaly detection algorithms. You are proficient in implementing and fine-tuning machine learning models to achieve high accuracy and reliability in fraud detection tasks.
- You excel in analyzing large-scale, complex datasets to extract meaningful insights and identify patterns indicative of fraudulent behavior. You are adept at exploratory data analysis, feature engineering, and statistical analysis to uncover hidden trends and anomalies.
- You are proficient in Python and its key data science-related libraries (scikit-learn, TensorFlow, PyTorch, pandas, NumPy).
- You have experience with cloud platforms, preferably Databricks.
- You have a proven track record of designing and automating scalable data processing pipelines and infrastructure.
- You are a strong communicator and enjoy working with cross-functional teams, including data scientists, software engineers, and business stakeholders.
- You excel at solving complex technical challenges related to deploying and managing machine learning models in production. You are adept at troubleshooting issues, optimizing performance, and ensuring the robustness and reliability of production systems.
- You demonstrate leadership potential, whether through mentoring junior team members, leading technical initiatives, or driving innovation within the organization. You are passionate about sharing your knowledge and expertise to help others grow and succeed in their roles.
Bonus Points
- Publications or research experience in fraud detection or anomaly detection.
- Experience working in fraud detection.
- PhD Degree in Computer Science, Statistics, Mathematics, or a related quantitative field of study.
