Data Scientist
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Job Description
Data Scientist – Bangalore
No. of Position: 4
Preferred to be in onshore, if not offshore India but candidate should be based out of Bangalore
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)
No. of Position: 4
Preferred to be in onshore, if not offshore India but candidate should be based out of Bangalore
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)
More Info
Key Skills
Feature engineering
Data pipelines
Scikit-learn
Model evaluation
About Company
Skills:
Machine Learning, Python Programming, Heat exchanger design, Distillation systems, Heat Transfer, Engineering software, Process Systems Engineering, Chemical Engineering
Skills:
Time Series Analysis, Machine Learning, Sql, Tensorflow, Numpy, Pandas, Pytorch, Python, scikit-learn, probabilistic forecasting, Statistics, statsmodels
Skills:
Tensorflow, Pytorch, Python
Skills:
Machine Learning, Data Science, data mining, Etl, Statistical Modeling, causal models, analytics techniques, conversion models
Skills:
Data Manipulation, Data Transformation, Python Programming, data pipeline development, logic-based workflows, Analytics, SQL proficiency, Dataiku, common coding tools within Dataiku, Excel-like analytical methods