Data Scientist

Location: Prakāshamnagar

Job Title:

Data Scientist (Product & Algorithms)

Location:

Delhi (Hybrid/Remote)
Job Type:  Full-time

About Us  At Best Nanotech, we are on a mission to [insert mission]. We generate massive amounts of data every day, but data without insight is just overhead. We are building a high-performance Data Science team to help us make smarter, faster decisions.

The Role  We are looking for a  Data Scientist  who sits at the intersection of coding, statistics, and business strategy.
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You won't just be handed a ticket to 'build a model.' You will partner with Product Managers and Engineers to identify problems, formulate hypotheses, and prototype solutions that scale.

Key Responsibilities
Exploratory Analysis:  Mine large datasets to uncover trends, patterns, and user behaviors that drive product innovation.
Modeling:  Build and validate predictive models (Regression, Classification, Clustering) using  Python (Scikit-Learn/Pandas) .
Experimentation:  Design, execute, and analyze  A/B tests  to validate product changes.
Data Wrangling:  Write complex  SQL  queries to extract data and build lightweight ETL pipelines for your analysis.
Storytelling:  Visualize your findings (using Tableau, Looker, or Matplotlib) and present actionable recommendations to stakeholders.
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What You Bring (Requirements)

Experience:

3+ years of experience in a Data Science or Analytics role.

Education:

Degree in Math, Statistics, Computer Science, Economics, or related quantitative field.
The Toolkit:
Expert-level  SQL  (Joins, Window Functions, CTEs).
Strong proficiency in  Python  (preferred) or  R .

Experience with libraries like Pandas, Num Py, Scikit-learn.
The Math:  Solid grasp of statistics (Hypothesis testing, distributions, bias/variance).
The

Soft Skills:

Ability to translate complex technical results into plain English for non-technical partners.
Bonus Points

Experience with Cloud Data Warehouses (Snowflake, Big Query).
Experience deploying models as APIs (Flask/FastAPI).
Familiarity with causal inference techniques.

How to Apply

Click  Easy Apply  or send your portfolio/Git Hub link to [HIDDEN TEXT]