Staff Data Scientist
Oscilar, an AI Risk Decisioning platform for banks and fintechs, is hiring a Staff Data Scientist to build and optimize fraud detection models. Day to day: developing ML/statistical models, analyzing large datasets with tools like Spark and Dask, and partnering with engineering and product using Python and cloud/MLOps tooling.
- Compensation
- $210K–$250K / yr
- Location
- United States
- Work setup
- Remote
- Seniority
- Staff
- Employment
- Not specified
- Experience
- 5+ years
- Category
- Data science
- Posted
Skills
Must have
- 5+ years of experience in data science, machine learning, or a related field, with a focus on fraud prevention and/or anti-money laundering
- Proficiency in Python
- Strong knowledge of machine learning algorithms and statistical techniques, with a focus on their application in fraud detection
- Experience working with large datasets using distributed systems like Apache Spark and Dask, including data cleaning, quality, transformation, and feature engineering at scale
- Excellent analytical and problem-solving skills, with the ability to derive actionable insights from complex data
- Strong communication skills with both technical and non-technical audiences
- Ability to work independently and collaboratively in a fast-paced, dynamic startup environment
Nice to have
- Fluency in cloud development (AWS, GCP, Azure, etc.) and MLOps
- Experience in the fintech, marketplaces, or financial services industry
- Knowledge of current fraud tactics and trends, and experience with fraud detection tools and systems
Full description
Shape the future of trust in the age of AI
At Oscilar, we're building the most advanced AI Risk Decisioning™ Platform. Banks, fintechs, and digitally native organizations rely on us to manage their fraud, credit, and compliance risk with the power of AI. If you're passionate about solving complex problems and making the internet safer for everyone, this is your place.
Why join us:
- Mission-driven teams: Work alongside industry veterans from Meta, Uber, Citi, and Confluent, all united by a shared goal to make the digital world safer.
- Ownership and impact: We believe in extreme ownership. You'll be empowered to take responsibility, move fast, and make decisions that drive our mission forward.
- Innovate at the cutting edge: Your work will shape how modern finance detects fraud and manages risk.
Job Description
As a Data Scientist at Oscilar, you will be responsible for developing and implementing advanced fraud detection models to protect our customers’ business from fraudulent activities. As an early member in the ML team you will have great impact in building out our ML stack.
Responsibilities:
- Develop and implement advanced fraud detection models, leveraging machine learning and statistical techniques, to identify and prevent fraudulent activities across our platform.
- Collaborate with cross-functional teams, including engineering, product, and operations, to design and implement fraud detection systems and processes.
- Analyze large volumes of data to identify patterns, trends, and anomalies indicative of fraudulent behavior, and develop data-driven insights to improve fraud prevention strategies.
- Evaluate the performance of existing fraud detection models and systems, and continuously optimize and update them to adapt to changing fraud trends and tactics.
- Stay up-to-date with the latest trends and advancements in fraud detection, data science, and machine learning, and apply this knowledge to enhance our fraud prevention capabilities.
- Communicate complex data analysis and model performance results to both technical and non-technical stakeholders, driving data-driven decision-making across the organization.
- Ensure data privacy and security compliance in all aspects of fraud detection and data analysis.
Requirements:
- 5+ years of experience in data science, machine learning, or a related field, with a focus on fraud prevention and/or anti-money laundering.
- Proficiency in Python.
- Fluency in cloud development (AWS, GCP, Azure, etc.) and MLOps is a plus.