Zeno HealthMumbai INR 9 - 16 LPA Experience : 2 - 8 YRS. Openings: 2
Job Description – Data Scientist
We are looking for Data Scientists to work on exciting problems in Supply Chain domain. You will be
one of the Data Scientists at the organization and will enjoy working with one of the richest data sets
in the industry, open-source tools, and the ability to see your insights turned into real products every
day. You will be primarily responsible for developing, testing, and deploying ML models in
consultation with founders, stakeholders, and other end users to create high value impact.
At ZenoHealth, we provide you.
• Make a big impact our consumer life
• Opportunity to join a fast-growing dynamic, fast-paced start-up environment.
• We are a young, motivated, experienced team of top performers.
• Work with domain specialists, IITians & IIMs
• We’re always learning and pushing boundaries, for us that’s the first step to a fulfilling
• Collaborative work environment
• Open cubicle environment
• Potential to move to a Lead role as the team expands.
• The open environment to work on most complex problems in start-ups.
• For some leisure time we play Foosball & Carrom
• And if you are a bookworm, we got your back as we have a small library for you.
Qualification : BE/B.Tech form Primier Institute
Python, Machin Learning, Data Extraction, SQL, Data Transformation
In addition, as a Data Scientist,
• your work will change healthcare delivery in India impacting millions of patients in India.
• you will be working on stimulating industry-first solutions with diverse team members.
• Machine learning: 3+ years of professional experience doing quantitative analysis using ML
• Python: Proven skills in building, manipulating, and deploying models in Python.
• SQL: Experience in writing complex yet efficient SQL queries.
• Data manipulation: Experience in handling large volumes of data including data extraction, and
• Familiarity working with any RDBMS or preferably MySQL.
• Good at MS Excel.
• Design, develop and manage predictive models to solve inventory management problems and improve user engagement using machine learning.
• Monitor key product metrics, understanding root causes of changes in metrics.
• Conduct hypothesis driven exploratory analysis and select the most appropriate modelling
techniques which include a variety of machine learning models.
• Communicate complex concepts and the results of the analyses in a clear and effective manner.
• Partner with various stakeholders – Operations, Stores – within organization to improve process
• Managing ambiguity and reacting quickly to changing priorities even when not all the information
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