Data Scientist III

Key Skills

Java Programming, Python Programming, SQL, Machine learning techniques, Deep Learning, Natural Language Processing (NLP), R Programming, PySpark

Job Description

S&P Global Market Intelligence

The Role: Data Scientist III

The Team: MI Data Science team is responsible for enhancing core capabilities of S&P Market Intelligence via data science solutions. The team works closely with other business units providing powerful insights using differentiated capabilities like Entity recognition, Document linking, Customer Analytics, Climate analytics, Quantitative analysis, Knowledge Graphs, Recommender System etc.

The Impact: This is an IC role which would assist the team in developing analytical solutions to business problems

What’s in it for you: Candidate will work closely with highly impactful team (US Patent holders and Ivy League Professors) who has diverse range of experiences. He/ she will collaborate with ML Engineers, Data Engineers, Visualization experts and other data scientists. He/she will get opportunities to publish technical papers and present at paramount international conferences.

Responsibilities:

• Gathering requirements and understanding the problem
• Sourcing data with help of data engineers
• Develop analytical solutions that impact directly to S&P goals/vision and
• Communicating results to stakeholders.

What We’re Looking For:

• 6+ years’ industry experience ( or 3+ years with a post-graduate degree in a quantitative discipline such as applied mathematics, computer science, statistics etc. ) in a data science, advanced analytics or machine learning team.
• A Bachelor’s Degree in Computer Science, Mathematical or Statistical sciences or related quantitative disciplines is required.
• Proficiency in Python and anyone of the following programming languages: R, Pyspark
• Proficiency in applied machine learning, optimization, statistical and probabilistic modeling techniques in relevant problem areas
• Strong understanding and experience of Natural Language Processing / Deep Learning / Deep Reinforcement Learning / AI concepts and exposure of taking ML models into production.
• Strong interpersonal and communication skills and ability to communicate with cross-functional stakeholders.
• Ability to interpret and explain model predictions and insights to varied audiences.
• Ability to thrive in an unstructured environment, working autonomously on a strong team to find opportunity and deliver business impact.
• Examples of relevant domain expertise: predictive modeling, customer journey modeling, computational advertising, demand forecasting, recommender systems, cohort analysis and insights generation, graph/text mining