Serving Skill | Jobs | Senior Data Scientist – SQL/Python/Numpy | BigDataKB.com | 31-01-22

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Job Location: Bengaluru

– Bachelor’s or Master’s degree in a quantitative field (CS, machine learning, mathematics, statistics) or equivalent experience.

– Good programming skills in Python Strong working knowledge of Python’s numerical, data analysis, or AI frameworks such as NumPy, Pandas, Scikit-learn, Jupyter, etc.

– Experience applying various machine learning techniques and understanding the key parameters that affect their performance.

– Experience developing experimental and analytic plans for data modeling processes, use of strong baselines, and the ability to accurately determine cause and effect relationships.

– Have a history of building systems that capture and utilize large data sets to quantify performance via metrics or KPIs.

– Understanding of relevant statistical measures such as confidence intervals, the significance of error measurements, development, and evaluation data sets, etc.-

– Experienced in using multiple data science methodologies to solve complex business problems.

– Experienced in handling large data sets using SQL and databases in a business environment.

– Excellent verbal and written communication. Strong troubleshooting and problem-solving skills.

– Thrive in a fast-paced, innovative environment.

Location : Bangalore, Karnataka, India

– Data Science, Deep learning, ML/Model

– Imoka – SQL, Python – 30min online test

– Look people only from Data Science Background

– If people are from Retail background it’s an added advantage

– Requirement is open for Both SBA & AC

Senior Data Scientist Responsibilities :

– Formulating, suggesting, and managing data-driven projects which are geared at furthering the business’s interests.

– Collating and cleaning data from various entities for later use by junior data scientists.

– Delegating tasks to Junior Data Scientists in order to realize the successful completion of projects.

– Monitoring the performance of Junior Data Scientists and providing them with practical guidance, as needed.

– Selecting and employing advanced statistical procedures to obtain actionable insights.

– Cross-validating models to ensure their generalizability.

– Producing and disseminating non-technical reports that detail the successes and limitations of each project.

– Suggesting ways in which insights obtained might be used to inform business strategies.

– Staying informed about developments in Data Science and adjacent fields to ensure that outputs are always relevant.

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