Asks for

The ad says“Experience working with Agile teams, using tools like Jira, Confluence, Slack.”
The ad says“AWS (SageMaker, Redshift), GCP (Vertex AI, BigQuery), Azure (ML Studio, Data Lake).”
The ad says“AWS (SageMaker, Redshift), GCP (Vertex AI, BigQuery), Azure (ML Studio, Data Lake).”
The ad says“Hypothesis testing, regression models, Bayesian inference, multivariate analysis.”
The ad says“SQL, NoSQL (MongoDB, Cassandra), cloud-native databases (BigQuery, Redshift, Snowflake).”
The ad says“SQL, NoSQL (MongoDB, Cassandra), cloud-native databases (BigQuery, Redshift, Snowflake).”
The ad says“Data preprocessing, cleaning, feature engineering, large dataset handling.”
The ad says“Supervised/unsupervised learning, time series forecasting, clustering, NLP, deep learning (optional).”
The ad says“Experience working with Agile teams, using tools like Jira, Confluence, Slack.”
The ad says“AWS (SageMaker, Redshift), GCP (Vertex AI, BigQuery), Azure (ML Studio, Data Lake).”
The ad says“Data preprocessing, cleaning, feature engineering, large dataset handling.”
The ad says“MLflow, Docker, Kubernetes (for advanced roles).”
The ad says“Data preprocessing, cleaning, feature engineering, large dataset handling.”
The ad says“AWS (SageMaker, Redshift), GCP (Vertex AI, BigQuery), Azure (ML Studio, Data Lake).”
The ad says“Git, GitHub/GitLab.”
The ad says“Git, GitHub/GitLab.”
The ad says“Spark, Hadoop, & Hive.”
The ad says“Spark, Hadoop, & Hive.”
The ad says“Hypothesis testing, regression models, Bayesian inference, multivariate analysis.”
The ad says“Experience working with Agile teams, using tools like Jira, Confluence, Slack.”
The ad says“Python (pandas, scikit-learn, NumPy, matplotlib, seaborn), R, SQL.”
The ad says“AWS (SageMaker, Redshift), GCP (Vertex AI, BigQuery), Azure (ML Studio, Data Lake).”
The ad says“MLflow, Docker, Kubernetes (for advanced roles).”
The ad says“SQL, NoSQL (MongoDB, Cassandra), cloud-native databases (BigQuery, Redshift, Snowflake).”
The ad says“Hypothesis testing, regression models, Bayesian inference, multivariate analysis.”
The ad says“Supervised/unsupervised learning, time series forecasting, clustering, NLP, deep learning (optional).”
The ad says“SQL, NoSQL (MongoDB, Cassandra), cloud-native databases (BigQuery, Redshift, Snowflake).”
The ad says“Python (pandas, scikit-learn, NumPy, matplotlib, seaborn), R, SQL.”
The ad says“Python (pandas, scikit-learn, NumPy, matplotlib, seaborn), R, SQL.”
The ad says“Power BI, Tableau, Plotly, Matplotlib, Seaborn.”
The ad says“Power BI, Tableau, Plotly, Matplotlib, Seaborn.”
The ad says“Python (pandas, scikit-learn, NumPy, matplotlib, seaborn), R, SQL.”
The ad says“Python (pandas, scikit-learn, NumPy, matplotlib, seaborn), R, SQL.”
The ad says“SQL, NoSQL (MongoDB, Cassandra), cloud-native databases (BigQuery, Redshift, Snowflake).”
The ad says“Hypothesis testing, regression models, Bayesian inference, multivariate analysis.”
The ad says“AWS (SageMaker, Redshift), GCP (Vertex AI, BigQuery), Azure (ML Studio, Data Lake).”
The ad says“Python (pandas, scikit-learn, NumPy, matplotlib, seaborn), R, SQL.”
The ad says“Python (pandas, scikit-learn, NumPy, matplotlib, seaborn), R, SQL.”
The ad says“Experience working with Agile teams, using tools like Jira, Confluence, Slack.”
The ad says“SQL, NoSQL (MongoDB, Cassandra), cloud-native databases (BigQuery, Redshift, Snowflake).”
The ad says“Spark, Hadoop, & Hive.”
The ad says“Python (pandas, scikit-learn, NumPy, matplotlib, seaborn), R, SQL.”
The ad says“Supervised/unsupervised learning, time series forecasting, clustering, NLP, deep learning (optional).”
The ad says“Power BI, Tableau, Plotly, Matplotlib, Seaborn.”
The ad says“Supervised/unsupervised learning, time series forecasting, clustering, NLP, deep learning (optional).”
The ad says“AWS (SageMaker, Redshift), GCP (Vertex AI, BigQuery), Azure (ML Studio, Data Lake).”

Company Description

Company Introduction – T-Systems ICT India Pvt. Ltd. T-Systems Information and Communication Technology India Private Limited (T-Systems ICT India Pvt. Ltd.) is a certified Great Place To Work®, proudly recognized for its strong people-first culture and commitment to employee excellence. As a wholly owned subsidiary of T-Systems International GmbH, T-Systems India operates out of Pune and Bangalore, with a dynamic team of over 4,200 professionals delivering high-value IT services to group customers worldwide. T-Systems India plays a key role in this global vision by delivering integrated, end-to-end IT solutions and sector-specific software to drive transformation across industries, including automotive, manufacturing, logistics, transportation, healthcare, and the public sector. For over 25 years, T-Systems International GmbH has been at the forefront of digital innovation, driving progress and fostering digital optimism. As a leading European IT services provider and a proud part of Deutsche Telekom, T-Systems delivers transformative digitalization projects backed by deep expertise in consulting, cloud, AI, cybersecurity, and connectivity. With a global workforce of 26,000 employees across 26 countries, we set industry benchmarks in efficiency, sovereignty, security, and reliability—empowering organizations to unlock their full digital potential. With annual revenues exceeding EUR 4.0 billion (2024), T-Systems stands as one of Europe’s foremost digital transformation partners, committed to shaping the future of enterprise technology.

Job Description

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.

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, 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.

Additional Information

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.

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Asks for

Values

Company Description

Job Description

Additional Information