Logo
4 Staffing Corp

Experienced Data Scientist

4 Staffing Corp, New York, New York, us, 10261


About the job Experienced Data Scientist

Our client is looking for a highly skilled individual with a background in experimentation and causal inference to join their team. The role involves developing advanced data science solutions using machine learning and artificial intelligence to drive innovation across various business areas and products. Collaboration with senior executives on impactful AI/ML projects to enhance risk management and overall financial performance is a key aspect of this position. Successful candidates will bring expertise in relevant industries, a passion for applying cutting-edge ML and AI techniques, and the ability to design and implement data science solutions that promote growth, competitive advantage, and customer satisfaction.

Qualifications:

A PhD with 2+ years of experience or a Master's degree with 4+ years of experience in fields such as Statistics, Computer Science, Engineering, Applied mathematics, or related disciplines. Hands-on experience of at least 3 years in ML modeling and development. Strong theoretical understanding of probability and statistics, as well as expertise in causal inference techniques. Proven ability to formulate hypotheses for assessing consumer behavior, design, execute, and deploy experiments. Proficiency in Python programming, with skills in PyTorch and/or Tensorflow. A solid background in algorithms and familiarity with various ML models. Exceptional communication skills and the capacity to collaborate effectively across different teams, including Product, Engineering, and other functional areas, at both leadership and hands-on levels. Strong analytical and problem-solving capabilities with acute attention to detail. Demonstrated leadership in providing technical guidance and mentoring to data scientists, along with effective management skills for tracking and monitoring performance to ensure success within the organization.

Locations: Northeastern USA - Hybrid/Remote (2-3 days per/week)