Company Name:-
S&P Global

Job Location:-
Hyderabad, Telangana

Job Summary:-
Segment : S&P Global Market Intelligence
The Role: Lead Data Scientist
Grade ( relevant for internal applicants only ): 11A
The Location: Gurgaon/Hyderabad/Remote Worker
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, New Logo Predictions, Climate analytics, Quantitative analysis, Knowledge Graphs 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:
Primary responsibilities are

gathering requirements,

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:Basic Qualifications:
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 one of the following programming languages: R, Python.

Proficiency in applied machine learning, optimization, statistical and probabilistic modeling techniques in relevant problem areas

Strong theoretical understanding of statistical techniques, machine learning algorithms and their mathematical underpinnings.

Experience with the Python Data stack and machine learning frameworks (e.

g.

Numpy, Pandas, Dask, scikit-learn, mlr, caret, H2O, TensorFlow, PyTorch ,MLlib) is required.

Strong understanding and experience of Deep Learning / Deep Reinforcement Lear

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