Job DescriptionJob Description: Data ScientistsThe candidate should be a highly skilled Data Scientist with hands-on experience in building and deploying data-driven solutions. The ideal candidate should be comfortable working with large datasets, applying advanced statistical and machine learning techniques, and translating business problems into actionable insights.

Key Responsibilities:

- Analyze large volumes of structured and unstructured data to extract meaningful insights.
- Build, validate, and deploy machine learning models for predictive analytics and automation.
- Design and implement machine learning pipelines for structured and unstructured data.
- Work closely with business stakeholders and cross-functional teams including data engineers, & analysts to translate problems into data-driven solutions.
- Build dashboards and data visualizations to communicate findings effectively.
- Conduct testing, hypothesis validation, and experiment tracking.
- Optimize model performance and ensure scalability and maintainability in production environments.
- Document methodology, workflow, and results clearly for future reference and compliance.
- Continuously monitor model performance and retrain based on data drift and feedback loops.

Required Skills & Tools:

Category | Skills & Tools || :---------------- | :------------------------------------------------------------------------------------- || Programming | Python (pandas, scikit-learn, Num Py, matplotlib, seaborn), R, SQL. || Machine Learning | Supervised/unsupervised learning, time series forecasting, clustering, NLP, deep learning (optional). || Statistical Analysis | Hypothesis testing, regression models, Bayesian inference, multivariate analysis. || Data Engineering | Data preprocessing, cleaning, feature engineering, large dataset handling. || Big Data Tools | Spark, Hadoop, & Hive. || Model Deployment | MLflow, Docker, Kubernetes (for advanced roles). || Visualization | Power BI, Tableau, Plotly, Matplotlib, Seaborn. || Databases | SQL, NoSQL (MongoDB, Cassandra), cloud-native databases (Big Query, Redshift, Snowflake). || Cloud Platforms | AWS (Sage Maker,



Redshift), GCP (Vertex AI, Big Query), Azure (ML Studio, Data Lake). || Version Control | Git, Git Hub/Git Lab. || Collaboration | Experience working with Agile teams, using tools like Jira, Confluence, Slack. |Positive To have:

- Experience with NLP, GenAI/LLMs, or recommendation systems is a plus.
- Familiarity with MLOps practices and tools (e.g., Kubeflow, Airflow) is a bonus.

Qualifications

Additional Information

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Your Task

The role is for a Data Scientist who will be responsible for building and deploying data-driven solutions. The ideal candidate should be comfortable working with large datasets, applying advanced statistical and machine learning techniques, and translating business problems into actionable insights.

Responsibilities

- Analyze large volumes of structured and unstructured data to extract meaningful insights
- Build, validate, and deploy machine learning models for predictive analytics and automation
- Design and implement machine learning pipelines for structured and unstructured data
- Work closely with business stakeholders and cross-functional teams including data engineers and analysts to translate problems into data-driven solutions
- Build dashboards and data visualizations to communicate findings effectively
- Conduct testing, hypothesis validation, and experiment tracking




- Optimize model performance and ensure scalability and maintainability in production environments
- Document methodology, workflow, and results clearly for future reference and compliance
- Continuously monitor model performance and retrain based on data drift and feedback loops

Your Profile

We are looking for a highly skilled Data Scientist with expertise in machine learning, statistical analysis, and data engineering to build and deploy data-driven solutions while working with large datasets and cross-functional teams.

Must Have Skills

- Experience in Python programming with libraries such as pandas, scikit-learn, Num Py, matplotlib, and seaborn
- Knowledge of R programming language
- Proficiency in SQL
- Experience with supervised and unsupervised machine learning techniques
- Experience with time series forecasting
- Experience with clustering algorithms
- Experience with Natural Language Processing (NLP)
- Knowledge of statistical analysis including hypothesis testing, regression models, Bayesian inference, and multivariate analysis
- Experience in data preprocessing, cleaning, and feature engineering
- Experience handling large datasets
- Knowledge of big data tools such as Spark, Hadoop, and Hive
- Experience with model deployment using MLflow
- Experience with Docker
- Proficiency in data visualization tools including Power BI, Tableau, Plotly, Matplotlib, and Seaborn
- Experience with SQL and NoSQL databases (MongoDB, Cassandra)
- Experience with cloud-native databases (Big Query, Redshift, Snowflake)
- Experience with cloud platforms such as AWS (Sage Maker, Redshift), GCP (Vertex AI, Big Query), or Azure (ML Studio, Data Lake)
- Proficiency in version control using Git, Git Hub/Git Lab
- Experience working with Agile teams
- Experience using collaboration tools like Jira, Confluence, and Slack

Nice to Have Skills

- Experience with deep learning techniques
- Experience with NLP, GenAI/LLMs, or recommendation systems
- Experience with Kubernetes (for advanced roles)
- Familiarity with MLOps practices and tools (e.g., Kubeflow, Airflow)

📌 Data Scientist (m/f/d) (India)
🏢 T Systems
📍 India