Digital Technology Specialist - Data Science

bakerhughes; Mumbai; On-site; INR 1,500,000 - 2,300,000; Full time

Job summary

Baker Hughes seeks a Digital Technology Specialist to develop analytics addressing customer needs and opportunities. You will collaborate with software teams to translate algorithms into commercially viable products and services, working on applied, predictive, and prescriptive analytics.

You will perform exploratory data analyses, build statistical models, and create dashboards to communicate findings. The role emphasizes ML model deployment, MLOps, and responsible AI practices.

Qualifications

• Bachelor's or master's degree in data science, CS, statistics, mathematics, engineering, or 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.

Responsibilities

• Develop analytics to address customer needs and opportunities.
• Collaborate with software teams to translate algorithms into viable products and services.
• Work on applied analytics, predictive analytics, and prescriptive analytics.
• Perform exploratory data analyses using descriptive statistics and other methods.

Skills

Python

R

SQL

Scikit-learn

TensorFlow

PyTorch

XGBoost

Azure

AWS

GCP

Spark

Databricks

Kubernetes

Docker

MLflow

GenAI

RAG

Statistics

Time Series

Optimization

Education

Bachelor's degree

Master's degree

Tools

MLflow

Azure ML

Databricks

Kubernetes

Docker

Pinecone

Azure AI Search

Weaviate

Chroma

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