19,379 Data Science Roles jobs in India

Data Scientist experience (8 to 10 years)

• 5 years of relevant work experience as a data scientist
• Experience designing and building statistical forecasting models.
• Experience in Python, PySpark, Azure Machine Learning, OpenAI and SQL
• Minimum 2 years of experience in Azure Cloud using Natural Language API, MLflow
• Hands-on experience with LLMs and GenAI frameworks
• Experience designing and building machine learning models.
• Experience designing and building optimization models., including expertise with statistical data analysis
• Lead the end-to-end development of AI/ML solutions, from problem definition to production deployment.
• Strong programming skills in Python and experience with libraries like TensorFlow, PyTorch, Scikit-learn.
• Effective written and verbal communication skills

Mandatory: Skillset: Python, PySpark, Azure Machine Learning, OpenAI

Good to have : Azure Databricks

Position: Statistician

Experience Range: 2 to 4 yrs

Job Location: Bangalore

Work Mode: Hybrid (3 days in the office, 2 days remote)

About Anumana:

Anumana is a new AI-driven health technology company from nference, developing and delivering ECG algorithms enabling early diagnosis and intervention.

About the Role

We are looking for a detail-oriented Statistician to analyze data, identify trends, and support decision-making across departments. The ideal candidate has strong statistical modeling skills and experience in applying these techniques in real-world scenarios.

Minimum Qualifications:

• Bachelor’s/Master’s/PhD in Maths, statistics, or related technical field.
• Experience in effectively applying statistical methods on top of big data inferences to yield more relevant information.
• Fluency in R/Python for statistical work. Development familiarity such as making REST APIs, and scripting in Python would be preferable.
• Basic Knowledge of Data Science.

Preferred Qualifications:

• Proven relevant work experience.
• Proven strong record of applying statistics in different scenarios.
• Demonstrated ability to design and execute on R&D agenda.

Responsibilities:

• Join our data science and engineering team to undertake cutting-edge R&D projects in the above-mentioned areas.
• Interact with team members, including domain experts, to build and engineer optimal solutions for customer problems and platform features.
• Adapt and innovate on latest data science, statistics, and computer science techniques to develop solutions for real-world, large-scale problems in healthcare.

AB InBev GCC was incorporated in 2014 as a strategic partner for Anheuser-Busch InBev. The center leverages the power of data and analytics to drive growth for critical business functions such as operations, finance, people, and technology. The teams are transforming Operations through Tech and Analytics.

Do You Dream Big?

We Need You.

Job Description

Job Title: Data Scientist – Predictive Forecasting

Location: Bangalore

Reporting to: Senior Manager Analytics

1) Purpose of the role

We are seeking a talented Data Scientist specializing in Statistical modelling, Predictive modelling, Time Series Forecasting to join our dynamic analytics team. The ideal candidate will have a strong background in statistical modeling and machine learning, with a focus on time series analysis. You will be responsible for developing predictive models for Business Cycles and Monthly Performance Monitoring, generating actionable insights from Forecasts, and driving business value through data-driven decision-making.

2) Key tasks & accountabilities

• Develop and Implement Models:
• Design and implement and Maintain time series forecasting models to predict key business metrics.
• Utilize advanced statistical techniques and machine learning algorithms to improve model accuracy.
• Data Analysis and Insight Generation:
• Analyze large and complex datasets to identify trends, patterns, and anomalies.
• Generate actionable insights that inform business strategies and operational improvements.
• Collaborate with Cross-Functional Teams:
• Work closely with deployment and development data scientists, and other stakeholders to understand business needs.
• Communicate Findings:
• Present analysis results and insights to stakeholders in a clear and concise manner.
• Prepare reports, visualizations, and dashboards to communicate data findings.
• Continuous Improvement:
• Monitor model performance and implement enhancements as necessary.
• Stay updated with the latest developments in data science, machine learning, and time series forecasting.

3) Qualifications, Experience, Skills

Level of educational attainment required.

Bachelor’s or master’s degree in data science, Statistics, Mathematics, Computer Science, or a related field.

Previous work experience

• Minimum of 2 years of experience in data science or a related role.
• Proven experience with time series analysis and forecasting techniques is a plus

Technical Skills required

• Python (Data Structures, Control Flow, OOPs, Modules and Packages, Exception Handling, VENV)
• MLOPs Fundamentals (Model Development, VCS, CI, CD, Serving, Monitoring & Logging, Registry, Data and Model Lineage)
• Experience with machine learning frameworks (e.G., scikit-learn, TensorFlow is a plus).
• Familiarity with data visualization tools (e.G., Power BI, matplotlib, plotly, streamlet).
• Data Analysis Tools - Pandas, Excel (Pivot Tables, Charts, Macros, Conditional Formatting, Shortcuts)
• Insights presentation - PowerPoint
• Version Control System (Git) - Basic Commands, Branching and Merging, Pull Requests & Code Reviews

Other Skills required

• Excellent problem-solving and analytical skills.
• Strong communication and presentation abilities.
• Ability to work collaboratively in a team environment.

And above all of this, an undying love for beer!

We dream big to create future with more cheers.

Our focus revolves around elevating technology-driven enterprises to new heights. However, it's important to understand that our scope at Trinesis encompasses more than just software development. Our objective is to provide comprehensive assistance to startups and enterprises throughout every phase of their journey.

As our Team expands, we're actively seeking new talent. Currently, we're on the lookout for a skilled Senior Industrial Data Scientist to lead the development of robust statistical models for manufacturing forecasting and optimization. This is a hands-on technical leadership role focused on applying proven statistical methods, time-series analysis, and industrial process knowledge to solve real manufacturing problems with measurable business impact.

• Statistical Modeling & Analytics (40%)
• Time-Series Forecasting: Build robust forecasting models using ARIMA, exponential smoothing, and regression methods
• Statistical Process Control: Implement control charts, capability analysis, and process monitoring systems
• Predictive Maintenance: Develop survival analysis and degradation models for equipment failure prediction
• Anomaly Detection: Create statistical outlier detection and control limit-based monitoring systems
• Model Validation: Ensure statistical rigor, hypothesis testing, and confidence intervals for all predictions
• Technical Leadership (35%)
• Define Analytics Strategy: Lead the technical approach for industrial forecasting and optimization
• Production Systems: Ensure models are reliable, explainable, and deployable in industrial environments
• Team Mentoring: Guide junior analysts and engineers in statistical methods and industrial applications
• Quality Assurance: Establish validation processes and accuracy benchmarks for all models
• Tool Selection: Choose appropriate statistical software and deployment technologies
• Business Impact & Domain Expertise (15%)
• Manufacturing Domain: Understand and model complex manufacturing processes, failure modes, and quality parameters
• ROI Quantification: Translate ML predictions into measurable business value (cost savings, quality improvements)
• Client Collaboration: Work directly with manufacturing engineers and plant managers to understand requirements
• Model Validation: Ensure model accuracy and reliability in real-world manufacturing environments
• Team Building & Operations (10%)
• Team Leadership: Build and lead a high-performing data science team
• Process Establishment: Define ML development processes, model governance, and quality standards
• Cross-functional Collaboration: Work closely with data engineering, software engineering, and domain experts
• Knowledge Sharing: Establish documentation standards and knowledge transfer processes

• 7+ years of hands-on experience in data science and machine learning
• 3+ years in leadership or senior individual contributor roles
• 2+ years experience with real-time ML systems or industrial applications
• Statistical Methods & Analytics
• Time-Series Analysis: ARIMA, exponential smoothing, seasonal decomposition, trend analysis, Box-Jenkins methodology
• Statistical Process Control: Control charts (X-bar, R, CUSUM), process capability analysis, Six Sigma methods
• Regression Analysis: Linear/non-linear regression, logistic regression, robust regression methods
• Survival Analysis: Kaplan-Meier, Cox regression, Weibull analysis for equipment failure prediction
• Hypothesis Testing: t-tests, ANOVA, chi-square, non-parametric tests, power analysis
• Industrial Statistics: DOE (Design of Experiments), response surface methodology, reliability analysis
• Programming & Tools
• Python: Advanced proficiency (pandas, numpy, scipy, statsmodels, scikit-learn)
• R: Statistical analysis (preferred for advanced statistical modeling)
• SQL: Complex queries, time-series databases (InfluxDB, TimescaleDB)
• Statistical Software: Experience with SAS, SPSS, Minitab, or JMP
• Visualization: Statistical plots, process control charts (matplotlib, ggplot2, Tableau)
• Industry Experience
• Manufacturing/Industrial: 3+ years experience with manufacturing processes, quality control, or industrial operations
• Statistical Applications: Applied statistics in manufacturing, process industries, or quality improvement
• Industrial Data: Working with sensor data, process parameters, equipment monitoring systems
• Quality Systems: Six Sigma, Lean Manufacturing, ISO 9001, or similar quality frameworks

• Data Science
• Machine Learning
• Python
• R
• SQL

• • Great team of smart people, in a friendly and open culture.
• Competitive salary and benefits package.
• Opportunity for professional growth and advancement.
• Dynamic and collaborative work environment.
• Flexible working hours and remote work options.
• Various learning opportunities and training programs.

Each employee has a chance to see the impact of his work. You can make a real contribution to the success of the company.

Several activities are often organized all over the year, such as weekly sports sessions, team building events, monthly drink, and much more

A full-time position

Attractive salary package.

12 days / year, including

6 of your choice.

Play any sport with colleagues,

the bill is covered.

Fruit, coffee and

snacks provided.

• Statistical methods (descriptive & inferential)
• Probability distributions
• Regression and modeling
• Sampling theory
• Behavioral biases (decision science)
• Practical data application and software use

Job Type: Part-time

Expected hours: 5 per week

We are seeking for a 7-10 years experience AI engineer with a strong background in machine learning, programming skills, and a deep understanding of generative models. The position is responsible for turning research into practical solutions that address real-world problems while ensuring the reliability and ethical use of generative AI in their applications.

Technical Requirements:

Strong proficiency in Python for data processing and automation.

Handson experience with generative AI models and their integration into data workflows.

Handson experience with prompt engineering and LLM models (Opensource and Closesource)

Handson experience with Application development framework like LangChain, LangGraph etc.

Familiarity working with REST frameworks like Fast API, Angular, Flask and DJango.

Experience with cloud platforms (AWS, GCP, Azure) and related services is a plus.

Familiarity with containerization and orchestration tools (Docker, Kubernetes).

As a Data Analysis & Simulation Professional, the person will be responsible for:

Data Pipeline Development:

Design and implement scalable data pipelines using Python to ingest, process, and transform log data from various sources.

Generative AI Integration:

Collaborate with data scientists to integrate generative AI models into the log analysis workflow.

Develop APIs and services to deploy AI models for real-time log analysis and insights generation.

Data Monitoring and Maintenance:

Set up monitoring and alerting systems to ensure the reliability and performance of data pipelines.

Troubleshoot and resolve issues related to data ingestion, processing, and storage.

Collaboration and Documentation:

Work closely with cross-functional teams to understand requirements and deliver solutions that meet business needs.

Document data pipeline architecture, processes, and best practices for future reference and knowledge sharing.

Evaluation and Testing:

Conduct thorough testing and validation of generative models.

Research and Innovation:

Stay updated with the latest advancements in generative AI and explore innovative techniques to enhance model capabilities.

Experiment with different architectures and approaches.

Snowflake Utilization: (Good to have)

Design and optimize data storage and retrieval strategies using Snowflake.

Implement data modeling, partitioning, and indexing strategies to enhance query performance.

We are seeking for a 7-10 years experience AI engineer with a strong background in machine learning, programming skills, and a deep understanding of generative models. The position is responsible for turning research into practical solutions that address real-world problems while ensuring the reliability and ethical use of generative AI in their applications.

Technical Requirements:

Strong proficiency in Python for data processing and automation.

Handson experience with generative AI models and their integration into data workflows.

Handson experience with prompt engineering and LLM models (Opensource and Closesource)

Handson experience with Application development framework like LangChain, LangGraph etc.

Familiarity working with REST frameworks like Fast API, Angular, Flask and DJango.

Experience with cloud platforms (AWS, GCP, Azure) and related services is a plus.

Familiarity with containerization and orchestration tools (Docker, Kubernetes).

As a Data Analysis & Simulation Professional, the person will be responsible for:

Data Pipeline Development:

Design and implement scalable data pipelines using Python to ingest, process, and transform log data from various sources.

Generative AI Integration:

Collaborate with data scientists to integrate generative AI models into the log analysis workflow.

Develop APIs and services to deploy AI models for real-time log analysis and insights generation.

Data Monitoring and Maintenance:

Set up monitoring and alerting systems to ensure the reliability and performance of data pipelines.

Troubleshoot and resolve issues related to data ingestion, processing, and storage.

Collaboration and Documentation:

Work closely with cross-functional teams to understand requirements and deliver solutions that meet business needs.

Document data pipeline architecture, processes, and best practices for future reference and knowledge sharing.

Evaluation and Testing:

Conduct thorough testing and validation of generative models.

Research and Innovation:

Stay updated with the latest advancements in generative AI and explore innovative techniques to enhance model capabilities.

Experiment with different architectures and approaches.

Snowflake Utilization: (Good to have)

Design and optimize data storage and retrieval strategies using Snowflake.

Implement data modeling, partitioning, and indexing strategies to enhance query performance.

We are looking for Data Analytics trainees for our upcoming traineeship program.

Data Analytics Trainee Responsibilities:

• Week 1 & 2: Training Modules – Candidates will be completing the learning modules assigned.
• Week 3: Live Project – Candidates will be working on the live project assigned to them by the company.
• Week 4: Project Report – Candidates will be preparing a project report and submit.

Data Analytics Trainee Requirements:

• Bachelor's degree or pursuing
• .Proficiency with computers, especially MS Office
• .High level of accountability and motivation
• .Strong Interpersonal, time and project management, presentation, leadership, and communication skills
• .Creativity and ability to delegate responsibilities
• .Receptiveness to feedback and adaptability
• .Willingness to meet deadlines

.

Job Description:

We are looking for a skilled HR manager to oversee all aspects of Human Resources practices and processes. You will support business needs and ensure the proper implementation of company strategy and objectives. The goal is to promote corporate values and enable business success through human resources management, including job design, recruitment, performance management, training & development, employment cycle changes, talent management, and facilities management services.

Responsibilities:

• Enhances the organization's human resources by planning, implementing, and evaluating employee relations and human resources policies, programs, and practices.
• Maintains the work structure by updating job requirements and job descriptions for all positions.
• Supports organization staff by establishing and interviewing program; counselling managers on candidate selection; conducting and analyzing exit interviews; and recommending changes.
• Prepares employees for assignments by establishing and conducting orientation and training programs.
• Manages a pay plan by conducting periodic pay surveys; scheduling and conducting job evaluations; preparing pay budgets; monitoring and scheduling individual pay actions; and recommending, planning, and implementing pay structure revisions.
• Ensures planning, monitoring, and appraisal of employee work results by training managers to coach and discipline employees; scheduling management conferences with employees; hearing and resolving employee grievances; and counselling employees and supervisors.
• Implements employee benefits programs and informs employees of benefits by studying and assessing benefit needs and trends; recommending benefit programs to management; directing the processing of benefit claims; obtaining and evaluating benefit contract bids; awarding benefit contracts; and designing and conducting educational programs on benefit programs.
• Ensures legal compliance by monitoring and implementing applicable human resource federal and state requirements, conducting investigations, maintaining records, and representing the organization at hearings.