Data Scientist

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, NumPy, 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 (BigQuery, Redshift, Snowflake).
• Cloud Platforms
• AWS (SageMaker, Redshift), GCP (Vertex AI, BigQuery), Azure (ML Studio, Data Lake).
• Version Control
• Git, GitHub/GitLab.
• Collaboration
• Experience working with Agile teams, using tools like Jira, Confluence, Slack.
• Good 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.
• Please Note: Fraudulent job postings/job scams are increasingly common. Beware of misleading advertisements and fraudulent communication issuing 'offer letters' on behalf of T-Systems in exchange for a fee. Please look for an authentic T-Systems email id - XYZ@t-systems.com .
• Stay vigilant. Protect yourself from recruitment fraud!
• To know more please visit : Fraud Alert

Benefits

Additional Information

Job Description: Data Scientists The candidate should be 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.

Your Match

How well this role fits your profile.

Company Intel

What employees say

Worked at T-Systems ICT India Pvt. Ltd.? Share your experience