Digital Technology Specialist – Data Science

Jobtailor; Mumbai; On-site; INR 1,800,000 - 3,000,000; Full time

Job summary

Jobtailor in Mumbai seeks a data science/AI specialist to develop and deploy analytics, predictive models, and AI-driven solutions. You will collaborate across engineering teams to translate algorithms into viable products while ensuring data quality and governance.

The role requires building scalable pipelines, evaluating models, and communicating insights to stakeholders. Strong Python/R, SQL, and cloud experience are essential, with a focus on business outcomes.

Qualifications

• 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, and optimization techniques
• 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 healthcare, finance, retail, manufacturing, or telecommunications
• 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

Responsibilities

• Develop analytics to address customer needs and opportunities
• Collaborate with software developers and engineers to translate algorithms into commercially viable products and services
• Develop, deploy, and apply applied, predictive, 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 project artifacts to document, archive, and communicate 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 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, 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 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

Skills

Analytics

Python

SQL

ML frameworks

Communication

Education

Bachelor's or master's in data science/CS/Statistics/Math/Engineering

Tools

Scikit-learn

TensorFlow

PyTorch

XGBoost

Azure ML / Databricks / Kubernetes

Job description

• Develop analytics to address customer needs and opportunities


• Collaborate with software developers and engineers to translate algorithms into commercially viable products and services


• Develop, deploy, and apply applied, predictive, 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 project artifacts to document, archive, and communicate 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 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, 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 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

Requirements

• 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, and optimization techniques


• 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 healthcare, finance, retail, manufacturing, or telecommunications


• 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

Core Competencies

Demonstrates expertise in developing and deploying machine learning models, predictive analytics, and data-driven solutions while ensuring data quality and compliance. Proficient in collaborating with cross-functional teams to translate business needs into actionable insights and AI strategies.

Highest-signal resume keywords

• Machine Learning Model Development


• Predictive Analytics


• Data Quality Assessment


• Cloud Platforms: Microsoft Azure, AWS, Google Cloud Platform


• Programming Skills: Python and/or R

Hard Skills

• Statistical Analysis


• SQL and Database Technologies


• Data Visualization


• Exploratory Data Analysis


• MLOps Best Practices


• Generative AI Techniques


• Data Engineering


• Feature Engineering


• Hypothesis Testing


• Optimization Techniques

Soft Skills

• Problem-Solving


• Critical Thinking


• Communication Skills


• Collaboration


• Strategic Mindset

Industry Keywords

• Healthcare


• Finance


• Retail


• Manufacturing


• Telecommunications

Tools & Technologies

• Machine Learning Frameworks: Scikit-learn, TensorFlow, PyTorch, XGBoost


• MLOps Tools: MLflow, Azure ML, Databricks, Kubernetes, Docker


• Vector Databases: Pinecone, Azure AI Search, Weaviate, Chroma


• Data Warehousing


• ETL/ELT Pipelines