Core Responsibilities

· Problem Identification:

o Collaborate with business stakeholders to define high-value problems that can be solved using data.

o Translate vague business goals (e.g., "reduce churn") into specific data-driven objectives.

· Data Collection & Cleaning:

o Identify and pull data from various sources (SQL databases, APIs, web scraping, or internal logs).

o Perform Data Wrangling: Cleaning messy data, handling missing values, and ensuring data consistency for analysis.

· Exploratory Data Analysis (EDA):

o Use statistical methods to identify patterns, correlations, and anomalies.

o Visualise data to communicate initial findings and validate hypotheses before building complex models.

· Model Building & Evaluation:

o Select and train appropriate Machine Learning algorithms (e.g., Regression, Classification, Clustering, or NLP).

o Validate model performance using metrics like Accuracy, Precision,



Recall, or RMSE to ensure the model works on real-world data.

· Feature Engineering:

o Create new variables (features) from raw data to improve the predictive power of models.

· Insight Communication:

o Present findings to non-technical audiences through reports, slide decks, or interactive dashboards (e.g., Tableau, Power BI).

o Provide transparent recommendations based on data evidence rather than "gut feeling."

Key Skills

· Mathematics & Statistics: Linear algebra, calculus, and probability are the foundations of all ML algorithms.

· Programming: Proficiency in Python or R for data manipulation and modeling.

· Database Management: Writing complex SQL queries to extract data.

📌 Data Scientist (Bengaluru)
🏢 TalentOla
📍 Bengaluru