Sr./Staff Data Scientist
New
O
OscilarFintech / AI
Remote-first culture — work from anywhereFull-TimeSenior
Salary not disclosed
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Job Details
- Experience
- 3+ years
- Required Skills
- PythonCloud ComputingMachine LearningSparkMLOps
Requirements
- 3+ 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.
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.
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