Data Scientist
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Job Location:
Thiruvananthapuram - India
Monthly Salary:
Not Disclosed
Posted on:
13 hours ago
Vacancies:
1 Vacancy
Job Summary
Data Scientist
About the Role
The role requires a strong focus on data analysis machine learning model development and fraud
detection across large-scale datasets. The ideal candidate collaborates closely with engineering and
product teams to build scalable and reliable machine learning solutions that support data-driven
decision-making. Exposure to model development feature engineering experiment tracking and
modern MLOps practices is a strong advantage.
Key Responsibilities
1. Design develop and refine high-performance Fraud Prevention models using Python and
Gradient Boosting frameworks such as XGBoost LightGBM or CatBoost.
2. Manage the complete machine learning lifecycle including data extraction feature engineering
model training evaluation and deployment support.
3. Conduct data research behavioural analysis and performance benchmarking on production
datasets.
4. Write and optimize SQL queries to extract and analyse data from PostgreSQL databases for
model development and validation.
5. Utilize MLflow for experiment tracking model versioning and ensuring reproducibility across
development stages.
6. Maintain code integrity and collaborative workflows using Git and Bitbucket.
7. Work within Linux environments and utilize shell scripting (Bash) to automate workflows and
operational tasks.
8. Develop visualizations and analytical insights using data visualization tools.
9. Collaborate with cross-functional teams to improve model performance and data-driven decision
making.
10. Ensure data privacy security and compliance best practices while working with production data.
1. Design develop and refine high-performance Fraud Prevention models using Python and
Gradient Boosting frameworks such as XGBoost LightGBM or CatBoost.
2. Manage the complete machine learning lifecycle including data extraction feature engineering
model training evaluation and deployment support.
3. Conduct data research behavioural analysis and performance benchmarking on production
datasets.
4. Write and optimize SQL queries to extract and analyse data from PostgreSQL databases for
model development and validation.
5. Utilize MLflow for experiment tracking model versioning and ensuring reproducibility across
development stages.
6. Maintain code integrity and collaborative workflows using Git and Bitbucket.
7. Work within Linux environments and utilize shell scripting (Bash) to automate workflows and
operational tasks.
8. Develop visualizations and analytical insights using data visualization tools.
9. Collaborate with cross-functional teams to improve model performance and data-driven decision
making.
10. Ensure data privacy security and compliance best practices while working with production data.
Required Skills
1. 5 8 years of overall experience in Data Science Machine Learning or related roles.
2. 3 5 years of hands-on experience in Python-based data science and machine learning.
3. Strong proficiency in Python and data science libraries such as Pandas NumPy and Scikit-learn.
4. 1 2 years of experience working with Gradient Boosting frameworks such as XGBoost
LightGBM or CatBoost.
5. Strong knowledge of SQL and PostgreSQL for data extraction and analysis.
1. 5 8 years of overall experience in Data Science Machine Learning or related roles.
2. 3 5 years of hands-on experience in Python-based data science and machine learning.
3. Strong proficiency in Python and data science libraries such as Pandas NumPy and Scikit-learn.
4. 1 2 years of experience working with Gradient Boosting frameworks such as XGBoost
LightGBM or CatBoost.
5. Strong knowledge of SQL and PostgreSQL for data extraction and analysis.
6. Hands-on experience with MLflow for experiment tracking and model versioning.
7. Experience with Jupyter Notebook or JupyterHub for model experimentation and data
exploration.
8. Proficiency in Git and Bitbucket for version control and collaborative development.
9. Familiarity with Linux/Unix environments and basic Shell scripting.
10. Understanding of machine learning techniques including classification anomaly detection and
feature engineering.
11. Knowledge of data visualization tools such as Plotly Matplotlib or Seaborn.
12. Strong analytical thinking problem-solving ability and attention to detail.
13. Good communication and collaboration skills.
7. Experience with Jupyter Notebook or JupyterHub for model experimentation and data
exploration.
8. Proficiency in Git and Bitbucket for version control and collaborative development.
9. Familiarity with Linux/Unix environments and basic Shell scripting.
10. Understanding of machine learning techniques including classification anomaly detection and
feature engineering.
11. Knowledge of data visualization tools such as Plotly Matplotlib or Seaborn.
12. Strong analytical thinking problem-solving ability and attention to detail.
13. Good communication and collaboration skills.
Kindly Note :- Added Advantage
1. Experience working with large datasets or big data technologies such as Spark or Dask.
2. Prior experience in Fintech Banking or Cybersecurity domains.
3. Understanding of MLOps concepts including model deployment and monitoring in production
environments.
4. Familiarity with package management tools such as Conda Pip or virtual environments.
5. Knowledge of data privacy and security best practices when handling production data.
1. Experience working with large datasets or big data technologies such as Spark or Dask.
2. Prior experience in Fintech Banking or Cybersecurity domains.
3. Understanding of MLOps concepts including model deployment and monitoring in production
environments.
4. Familiarity with package management tools such as Conda Pip or virtual environments.
5. Knowledge of data privacy and security best practices when handling production data.
