Data Scientist

Awign; Bengaluru; On-site; INR 1,000,000 - 1,500,000; Full time

Job summary

A technology firm seeks a skilled Data Scientist based in Bengaluru. The ideal candidate will have 5-6 years of experience with strong Python proficiency and hands-on experience in Machine Learning, particularly in regression and classification. Responsibilities include deriving insights from large datasets and converting business problems into data-driven solutions. Familiarity with cloud platforms and data visualization tools is preferred, along with strong analytical and communication skills.

Qualifications

• 5–6 years of experience in a data science role.
• Ability to convert business problems into data‑driven solutions.
• Experience working with large, real‑world datasets.

Skills

Strong proficiency in Python (Pandas, NumPy, Scikit‑learn)

Strong SQL skills for data extraction and analysis

Hands‑on experience in Machine Learning

Solid understanding of statistics and probability

Experience in data cleaning, feature engineering, and model evaluation

Knowledge of time series analysis and forecasting

Tools

Python libraries: Scikit‑learn, TensorFlow / PyTorch

Data visualization: Power BI, Tableau, Matplotlib, Seaborn

Big data exposure: Spark / PySpark

Version control: Git / GitHub

Cloud exposure: AWS, Azure, or GCP

Data platforms: Snowflake / BigQuery / Redshift

Understanding of ETL and data pipelines

Job description

Data Scientist – Bangalore

No. of Position: 4
Location: Candidate should be based out of Bangalore (onshore preferred, offshore India acceptable)

Interview Process: Single round, Face-to-Face only

Experience: 5–6 years

Profile Expectation: Strong hands‑on, hardcore development‑oriented candidates

Required Skills:

• Strong proficiency in Python (Pandas, NumPy, Scikit‑learn)
• Strong SQL skills for data extraction and analysis
• Hands‑on experience in Machine Learning (regression, classification, clustering)
• Solid understanding of statistics and probability
• Experience in data cleaning, feature engineering, and model evaluation
• Knowledge of time series analysis and forecasting

Tools & Platforms:

• Python libraries: Scikit‑learn, TensorFlow / PyTorch (preferred)
• Data visualization: Power BI, Tableau, Matplotlib, Seaborn
• Big data exposure: Spark / PySpark (good to have)
• Version control: Git / GitHub
• Cloud exposure: AWS, Azure, or GCP
• Data platforms: Snowflake / BigQuery / Redshift (preferred)
• Understanding of ETL and data pipelines

Business & Domain Exposure:

• Ability to convert business problems into data‑driven solutions
• Experience working with large, real‑world datasets
• Strong analytical, communication, and stakeholder management skills
• Domain exposure to Banking, Insurance, Retail, or Telecom is a plus
• Experience in risk modeling, customer analytics, or fraud detection is desirable
• Awareness of data privacy and compliance standards (POPIA knowledge is an advantage)