What are the responsibilities and job description for the Data Scientist position at NITYA Software Solutions, Inc.?
Job Details
Immediately Interview !!!
Job Tittle : Data Scientist
Location : Mountain View, CA
On Site
Job description :
Must have strong in SQL and Essential to have Product Analytics background
Python(statistical and modeling libraries like Numpy, pandas, scikit-learn),
Technical Skills:
Advanced SQL skills and proficiency in visualization tools such as Qlik, Tableau, Plotly Dash
Strong analytical and modeling skills using Python (for its rich suite of statistical and modeling libraries like numpy, pandas, scikit-learn, etc.)
Familiarity with Linux/OS X command line, version control software (git), and general software development
BS or MS degree in Statistics, Mathematics, Operations Research, Computer Science, Econometrics or related eld; equivalent experience will be considered
7 years of experience in data science or product analytics, preferably in fintech, with a strong foundation in predictive modeling, customer segmentation, and experimentation
Ability to formulate data-backed strategies that will drive step-function growth for the business as well as increase customer benefit
Ability to generate hypotheses grounded in customer behavior, industry trends, and external market factors. Experience in the fintech or SMB space is highly preferred.
The ability to go deep on complex and vague requirements and get business moving recommendations from the data.
Experience in designing and interpreting complex experiments beyond traditional A/B testing methods, such as inference testing
Demonstrated experience in building reusable and scalable analytics solutions, with a focus on efficiency and avoiding duplication of work
Outstanding communication skills with the ability to influence decision makers and build consensus with teams
Quick learner, adaptable, with the ability to work independently or as part of a team in a fast-paced environment
Experience using statistics and machine learning techniques to solve complex business problems within go-to-market and marketing areas, e.g., propensity for feature adoption, customer health scoring to identify customers at risk to cancel, next best action models etc.
