Zorba is the new age analytics provider that brings data driven decision to life for its clients. We believe in getting analytics executed on ground with the client team rather than just creating complex machine learning or predictive modelling algorithms. 

We offer services across the spectrum ranging from analytics training and setting up data centres to delivering analytics based strategic consulting solutions. One of the key value adds we do is with the speed at which we deliver solutions.

Headquartered in Mumbai, Zorba was conceived with an objective of creating value for businesses in near future rather than long term. We are experts in leveraging big data, building data driven solutions and operationalizing analytics at each stage of decision making process with following key offerings: 

•	Data Visualization and Dashboard design
•	Predictive Modelling Algorithms
•	Data Management and Transformation
•	Analytics based reporting solutions
•	Marketing and Business Process Optimization

Witnessing the stories of success and failure of numerous businesses at different stages, we came to the conclusion that every business can be successful if improvisation continues relentlessly. 

We want to make this process faster and smarter for our clients in their quest for growth by hand holding through data analytics and strategic leadership for effective decision making. 

You can follow us on :
https://www.quora.com/profile/Yash-Chaudhary-516
https://www.instagram.com/zorbaconsulting/?hl=en
https://twitter.com/ConsultingZorba
https://www.facebook.com/Zorbaconsulting/

Zorba Consulting India

JD Summary – Data Scientist / Machine Learning Engineer
Experience: 3–5 years of relevant ML experience Education: B.Tech from a Tier-1 college preferred; MS/M.Tech in CS, IT, or a related field is an advantage.
1. Key Technical Skills
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Programming: Strong proficiency in at least one of Python, R, or Java; Python is highly relevant.
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Machine Learning &amp; Statistics: Strong understanding of ML algorithms, statistical concepts, hypothesis testing, and predictive/prescriptive modeling.
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Python Libraries: NumPy, Pandas, SciPy, Scikit-learn, IPython.
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Big Data &amp; Cloud: PySpark, AWS, Linux, Jupyter.
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Data Analysis: EDA, data extraction, data transformation, data preparation, and interpretation.
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2. Key Responsibilities
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Develop and validate statistical and machine learning models.
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Perform hypothesis testing, root cause analysis, factor analysis, and insight generation.
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Train and test models, improve accuracy, and monitor performance.
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Extract and transform data from enterprise data warehouses and big data platforms.
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Solve complex, unstructured business problems and engage with clients to understand business requirements.
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3. Required Soft Skills
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Strong problem-solving and analytical abilities.
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High ownership and willingness to learn.
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Team management capabilities.
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Comfortable working in a fast-paced, deadline-driven environment.
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Passion for technology and client-facing problem-solving.
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4. Ideal Candidate Profile
A Data Scientist / ML Engineer with 3–5 years of hands-on experience developing and validating ML models, strong statistical knowledge, proficiency in Python or another programming language, and practical exposure to data preparation, EDA, and big data technologies such as PySpark and AWS.