Digital Technology Specialist - Data Science

bakerhughes; Bengaluru; On-site; INR 1,300,000 - 1,900,000; Full time

Job summary

Baker Hughes is seeking a Digital Technology Specialist in Bengaluru to develop analytics and AI solutions. You will work with software teams to translate algorithms into deployable products and services, leveraging applied, predictive, and prescriptive analytics to influence business decisions.

You will build scalable data pipelines, deploy ML models, and create dashboards to present insights to both technical and non-technical audiences.

Qualifications

• Bachelor's or Master's in data science, CS, statistics, mathematics, engineering, or related field.
• Strong programming skills in Python and/or R.
• Expertise in SQL and database technologies.
• Experience with ML frameworks: Scikit-learn, TensorFlow, PyTorch, XGBoost.
• Experience with cloud platforms: Azure, AWS, GCP.

Responsibilities

• Develop analytics to address customer needs and opportunities.
• Translate algorithms into commercially viable products and services.
• Work in teams on applied analytics, predictive analytics, and prescriptive analytics.
• Build dashboards and visualizations to communicate findings.
• Develop, train, and deploy machine learning models.
• Create scalable data pipelines and implement MLOps best practices.
• Evaluate model performance and improve accuracy and reliability.
• Collaborate with stakeholders to define success metrics and business impact.

Skills

Python

R

SQL

ML frameworks

Cloud platforms

Data analysis

Communication

Education

Bachelor's or Master's in data science, CS, stats, math or related field

Quantitative field degree

Tools

TensorFlow

PyTorch

Scikit-learn

Spark

Databricks

Kubernetes

Docker

MLflow

Azure ML

Pinecone

Job description

As a Digital Technology Specialist, you will be responsible for:

• Develop analytics to address customer needs and opportunities.
• Work alongside software developers and software engineers to translate algorithms into commercially viable products and services.
• Work in technical teams in development, deployment, and application of applied analytics, predictive analytics, and prescriptive analytics.
• Perform exploratory and targeted data analyses using descriptive statistics and other methods.
• Work with data engineers on data quality assessment, data cleansing and data analytics
• Generate reports, annotated code, and other projects artifacts to document, archive, and communicate your work and outcomes.
• Share and discuss findings with team members.
• Analyze large, structured, and unstructured datasets to identify trends, patterns, and opportunities.
• Develop statistical models to uncover insights and support business decisions.
• Perform exploratory data analysis (EDA) and hypothesis testing.
• Create dashboards and visualizations to communicate findings.
• Design, develop, train, and deploy machine learning models.
• Build predictive, classification, recommendation, and optimization solutions.
• Apply Generative AI, LLMs, Retrieval-Augmented Generation (RAG), and Agentic AI techniques where applicable.
• Evaluate model performance and continuously improve accuracy and reliability.
• Build scalable data pipelines for data ingestion, transformation, and feature engineering.
• Implement model deployment, monitoring, and lifecycle management using MLOps best practices.
• Work with cloud-native AI and data platforms.
• Ensure data quality, governance, security, and compliance standards.
• Collaborate with business stakeholders to understand requirements and define success metrics.
• Translate business problems into analytical and AI solutions.
• Present technical findings to both technical and non-technical audiences.
• Measure and communicate business impact of deployed solutions.
• Stay current with advancements in AI, machine learning, generative AI, and analytics.
• Prototype new approaches and technologies.
• Contribute to AI strategy, governance, and responsible AI practices.

To be successful in this role you will have:

• Bachelor's or master's degree in data science, Computer Science, Statistics, Mathematics, Engineering, or a related quantitative field.
• Strong programming skills in Python and/or R .
• Expertise in SQL and database technologies.
• Experience with machine learning frameworks: Scikit-learn, TensorFlow, PyTorch, XGBoost.
• Experience with cloud platforms: Microsoft Azure, AWS, Google Cloud Platform.
• Knowledge of: Data Warehousing, Data Lakes, ETL/ELT pipelines, Spark and distributed computing.
• Familiarity with MLOps tools: MLflow, Azure ML, Databricks, Kubernetes, Docker.
• Experience with Generative AI, LLMs, Prompt Engineering, and RAG architectures.
• Strong understanding of: Statistics, Probability, Experimental Design, Time Series Analysis, Optimization Techniques.

Technical Skills

• Strong programming skills in Python and/or R .
• Expertise in SQL and database technologies.
• Experience with machine learning frameworks: Scikit-learn, TensorFlow, PyTorch, XGBoost.
• Experience with cloud platforms: Microsoft Azure, AWS, Google Cloud Platform.
• Knowledge of: Data Warehousing, Data Lakes, ETL/ELT pipelines, Spark and distributed computing.
• Familiarity with MLOps tools: MLflow, Azure ML, Databricks, Kubernetes, Docker.
• Experience with Generative AI, LLMs, Prompt Engineering, and RAG architectures.
• Strong understanding of: Statistics, Probability, Experimental Design, Time Series Analysis, Optimization Techniques.

Preferred Qualifications

• Experience deploying AI solutions into production environments.
• Knowledge of Responsible AI, AI governance, and model explainability.
• Experience with vector databases (Pinecone, Azure AI Search, Weaviate, Chroma).
• Familiarity with AI agents and autonomous workflows.
• Industry knowledge in domains such as healthcare, finance, retail, manufacturing, or telecommunications.

Soft Skills

• Strong problem-solving and critical-thinking abilities.
• Excellent communication and storytelling skills.
• Ability to work in cross-functional teams.
• Strategic mindset with a focus on business outcomes.
• Continuous learning and innovation mindset.

Success Metrics

• Delivers measurable business value from AI and analytics initiatives.
• Builds scalable and production-ready ML/AI solutions.
• Improves operational efficiency through data-driven automation.
• Drives adoption of AI technologies across the organization.
• Maintains high standards of governance, security, and responsible AI.

Work in a way that works for you We recognize that everyone is different and that the way in which people want to work and deliver at their best is different for everyone too. Working flexible hours - flexing the times when you work in the day to help you fit everything in and work when you are the most productive. Working wi