Job Description

Our client is looking for an experienced individual contributor with expertise in production-ready AI/ML solutions. In the role of Senior Data Scientist, you will be part of a team of Data Scientists, led by a Lead Data Scientist, focused on developing advanced data science solutions that utilize machine learning and artificial intelligence to drive innovation across various business lines and products. Collaboration with cross-functional teams in Data Science, Data Engineering, and Business groups is a key aspect of this role. We are seeking candidates with a strong technical foundation, coding proficiency, attention to detail, and a passion for analytical thinking and problem-solving.

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.
• A minimum of 3 years of hands-on experience in ML modeling and development.
• Strong understanding of data analysis and statistical modeling.
• Knowledge of various machine learning techniques (clustering, decision trees, bagging/boosting, artificial neural networks, etc.) and their real-world applications and limitations.
• Proven track record in experimental design and execution.
• Hands-on experience with data wrangling, including fuzzy matching and regular expressions, distributed computing, and parallelism in ML solutions.
• Proficient programming skills in Python.
• Solid background in algorithms and familiarity with a range of ML models.
• Excellent communication skills and the ability to collaborate effectively across different teams, from leadership to hands-on levels.
• Outstanding analytical and problem-solving abilities with meticulous attention to detail.
• Demonstrated leadership in providing technical guidance and mentoring to data scientists, along with effective management skills for monitoring and tracking performance to ensure success within the organization.

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