Data Science Manager
S
SentiLinkFinTech
United StatesFull-TimeManager
Salary210000 - 260000 USD per year
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Job Details
- Experience
- 10+ years relevant work experience & relevant Masters or 7+ years & relevant PhD. 2-5 years experience directly managing a team of data scientists, ideally in a startup environment. 3+ years of startup experience.
- Required Skills
- AWSPostgreSQLPythonMachine LearningRedshift
Requirements
- 10+ years relevant work experience & relevant Masters or 7+ years & relevant PhD.
- 2-5 years experience directly managing a team of data scientists, ideally in a startup environment.
- 3+ years of startup experience.
- Excellent communicator and team player.
- Proven track record of solving complex / high profile business problems with DS / ML solutions.
- Experience in communicating outcomes / progress to senior management / stakeholders.
- Very strong in “end to end” DS development: Planning, fleshing out success criteria / metrics, getting buy-in, developing the solution, delivering the solution (prod / deck / strategy doc / etc).
- Strong practical ML / Stats knowledge, i.e. can easily employ the suite of standard ML / stats tools to quickly scope out solutions, and double down where needed.
- Interest in developing deep domain expertise for product-focused work.
- Experience writing production code and tests.
- Detail oriented and thoughtful—someone we can rely on to make business-changing decisions.
- Thrive in a fast paced environment characterized by the need to solve extremely varied, high impact, open ended problems.
Responsibilities
- Directly manage a team of highly skilled data scientists. Start from 2-3 and grow to 5-6.
- Act as a technical mentor that can dive deep and provide detailed direction.
- Lead planning, resourcing and communications with senior leadership, product and engineering teams.
- Develop strong business intuition and guide your team to deliver performant DS solutions on aggressive timelines.
- Develop and maintain SentiLink’s fraud detection models through the full model development lifespan: from data acquisition decisions through featurization, focusing labeling resources, model training, experimentation, productionalization, and monitoring.
- Research new types of fraud and develop new SentiLink products around identity verification.
- Achieve success by researching / developing through iteration, integration of new data sources and inventive feature engineering.
- Write production-ready code that can be relied on for real-time decision making by our partners.
- Design, perform, and present analyses that will inform data acquisition, product development, risk operations priorities, marketing, and sales efforts.
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