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.
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.
Qualification
5+ years of experience in data scienceProficiency in PythonFluency in cloud development (AWSStrong communication skillsExperience in the fintech
Required
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.
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, handling data-related challenges such as data cleaning, data quality, and data 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 the ability to explain complex concepts and findings to both technical and non-technical audiences.
Ability to work independently and collaboratively in a fast-paced, dynamic startup environment.
Preferred
Experience in the fintech, marketplaces, or financial services industry.
Knowledge of current fraud tactics and trends, as well as experience with fraud detection tools and systems.
