Data Scientist - Python/Pandas

JD - Data ScientistExperience: 2 - 3 years

Location: Bangalore (On-site)

Notice Period: Candidates who can join immediately or within 30 days preferred.

(Note: Focus is strictly on classical ML, relational data, and business analytics-not suitable for profiles primarily focused on GenAI or LLMs.)Job Summary:

We are looking for a Data Scientist with 2 - 3 years of experience to join our team in Bangalore. In this role, you will bridge technical data workflows with core business operations, translating business problem statements into actionable analytical solutions. You will leverage strong hands-on SQL and Python skills to build baseline machine learning models and extract high-impact insights from relational datasets.

Must have skills:

• Technical Skills: 2 - 4 years of strong, hands-on experience in Python (Pandas, NumPy, Scikit-learn) and SQL for complex querying and data transformation.

• Business Understanding: Solid ability to grasp business contexts, identify operational pain points, and tie analytical outputs back to business KPIs.

• Core DS & ML: Strong foundational knowledge of applied statistics, hypothesis testing, exploratory data analysis, and baseline machine learning algorithms (regression, classification, clustering).

• Data Governance: Basic understanding of data governance, data quality standards, cataloging, lineage, and compliance principles.

• Communication: Clear communication and data storytelling skills to present technical outcomes to non-technical audiences.

Good to Have:

• Hands-on experience with visualization tools such as Power BI or Tableau.

• Basic exposure to building quick interactive prototypes using Streamlit.

• Familiarity with data pipelines, workflow automation, or basic model lifecycle management.

Key Responsibilities:

• Partner with stakeholders to break down complex business problems into structured analytical tasks.

• Write optimized SQL queries and Python scripts to extract, clean, preprocess, and validate datasets from relational databases.

• Build, evaluate, and interpret baseline machine learning and statistical models to support operational and strategic decision-making.

• Maintain data governance, data hygiene, and privacy standards across all analytics workflows.

• Deliver clear findings, reports, and visual summaries to non-technical business teams.