Systems Integration Senior Analyst at NTT DATA, Inc. in Bengaluru, IN-KA, India

Job description

• The Systems Integration Senior Specialist at NTT DATA in Bengaluru is responsible for integrating and analysing data across diverse domains, utilising Python and SQL for data engineering and machine learning. The role requires 3-4 years of experience in data science, machine learning, or related fields, and a bachelor’s or master’s degree in a quantitative discipline. Key responsibilities include data preparation, feature engineering, model evaluation, and presenting findings through visualisation tools such as Power BI or Tableau. Familiarity with business intelligence tools, cloud platforms (Azure, AWS, Google Cloud), and containerisation (Docker) is essential. Strong communication, problem-solving skills, and the ability to work in a distributed environment are also required. Exposure to deep learning frameworks, Spark, and advanced analytics is advantageous.
• Req ID: 383808
• NTT DATA strives to hire exceptional, innovative and passionate individuals who want to grow with us. If you want to be part of an inclusive, adaptable, and forward-thinking organization, apply now.

Responsibilities

• Integrating and analysing data across diverse domains.
• Data preparation, feature engineering, model evaluation, and presenting findings through visualisation tools such as Power BI or Tableau.

Requirements

• We are currently seeking a Systems Integration Senior Specialist to join our team in Bengaluru, Karnātaka (IN-KA), India (IN).
• Approximately 3-4 years of professional experience in data science, machine learning, data analytics, data engineering, or a related field.
• A bachelor’s or master’s degree in computer science, data science, statistics, mathematics, engineering, economics, or a related quantitative discipline.
• Strong working knowledge of Python for data analysis and machine learning.
• Pandas
• NumPy
• Scikit-learn
• Matplotlib or Plotly
• Good SQL skills, including joins, aggregations, window functions, common table expressions, and data validation.
• Probability and distributions
• Hypothesis testing
• Correlation and regression
• Classification and clustering
• Feature engineering
• Model evaluation
• Overfitting and regularization
• Cross-validation
• Experience performing exploratory data analysis and presenting findings through visualizations.
• Familiarity with data preparation, feature creation, missing-value treatment, and outlier handling.
• Experience with at least one business intelligence or visualization tool such as Power BI, Tableau, Looker, or similar.
• Basic understanding of Git, version control, and collaborative software development.
• Ability to communicate technical findings clearly to both technical and non-technical stakeholders.
• Strong problem-solving skills and attention to data accuracy.
• Ability to work effectively under the guidance of senior team members in a distributed environment.
• The ideal candidate will have practical exposure to several of the following areas:
• Machine learning algorithms for classification, regression, clustering, forecasting, recommendation, and anomaly detection.
• Statistical analysis, experiment design, A/B testing, and hypothesis validation.
• Deep learning frameworks such as TensorFlow, Keras, or PyTorch.
• Data-processing technologies such as Spark, Databricks, or cloud-based analytics platforms.
• Cloud services on Azure, AWS, or Google Cloud.
• Azure Machine Learning, Amazon SageMaker, Google Vertex AI, or equivalent model-development platforms.
• Experiment tracking and model-management tools such as MLflow or Weights & Biases.
• Containerization using Docker.
• APIs and model-serving frameworks such as FastAPI, Flask, or similar technologies.

Skills

Required

• Python
• SQL
• Pandas
• NumPy
• Scikit-learn
• Matplotlib or Plotly
• Power BI, Tableau, Looker, or similar
• Git
• Statistics and machine learning concepts
• Data preparation and feature engineering
• Exploratory data analysis
• Communication

Preferred

• TensorFlow, Keras, or PyTorch
• Azure, AWS, or Google Cloud
• MLflow or Weights & Biases
• Docker
• FastAPI, Flask, or similar