790 Data Science Roles jobs in Bangalore

• Acquire, process, and interpret remote sensing data (e.g., satellite imagery, LiDAR) for geological applications.
• Develop and apply geological models using integrated datasets, including geophysical and geochemical information.
• Conduct spatial analysis to identify potential resource targets and geological structures.
• Collaborate with geologists and geophysicists to interpret geological features and anomalies.
• Prepare detailed technical reports, maps, and presentations for stakeholders.
• Ensure the quality and accuracy of geological data and interpretations.
• Stay updated on the latest advancements in remote sensing technology and geological modeling software.
• Assist in the planning and execution of field exploration programs based on data analysis.
• Contribute to resource estimation and geological risk assessment.
• Maintain and manage geological databases and GIS projects.

• Master's or Ph.D. in Geological Engineering, Geosciences, or a related field with a focus on remote sensing and GIS.
• 5+ years of professional experience in geological exploration or mining.
• Proven expertise in processing and interpreting various types of remote sensing data.
• Proficiency in GIS software (e.g., ArcGIS, QGIS) and geological modeling software.
• Strong understanding of geological principles, mineralogy, and structural geology.
• Experience with programming languages like Python for data analysis is highly desirable.
• Excellent analytical, problem-solving, and critical thinking skills.
• Strong report writing and presentation skills.
• Ability to work collaboratively in a multidisciplinary team.
• Experience in the mining or oil and gas industry is a plus.

Freelance Opportunity: Statistician - Demographic & Synthetic Population Modeling (Remote 50,000/month)

Duration: 2-Month Contract (Renewable)

Compensation: 50,000/month (net, based on deliverables)

We are seeking a highly qualified freelance statistician to explore and analyze publicly available demographic datasets-including respondent-level microdata where available-and translate them into actionable statistical insights. The ultimate goal is to develop frameworks and schemas for generating synthetic populations of various Indian states.

This project is ideal for a candidate passionate about data exploration, sampling theory, and synthetic data generation for policy, market, or simulation purposes.

• Data Exploration & Cleaning
• Locate, access, and curate publicly available demographic datasets (census, NFHS, WVS, ASER, NSSO, etc.).
• Conduct thorough quality checks and handle missing or inconsistent data.
• Statistical Analysis & Modeling
• Perform distribution analysis, parameter estimation, and relationship mapping between demographic variables.
• Apply sampling techniques to design representative synthetic populations.
• Identify key demographic and behavioral parameters that inform population modeling.
• Schema & Synthetic Population Development
• Build statistical frameworks and schemas to simulate respondent-level data for Indian states.
• Develop replicable workflows and documentation for ongoing data updates.
• Reporting & Communication
• Present findings in clear, visualized formats (tables, charts, dashboards).
• Recommend refinements to improve the representativeness and robustness of the synthetic population models.

• Education: Master's degree or higher in Statistics, Econometrics, Data Science, or Quantitative Social Sciences from a premier institute (ISI, IITs, IISc, Delhi School of Economics, etc.).
• Core Expertise:
• Advanced understanding of sampling theory and experimental design.
• Strong proficiency in statistical programming (R, Python, or Stata).
• Knowledge of Bayesian inference and synthetic data generation techniques is a plus.
• Experience:
• Prior work with large demographic or respondent-level datasets.
• Track record of producing actionable statistical insights.

• Distribution and parameter analysis
• Survey sampling & weighting
• Regression and multivariate modeling
• Data wrangling and cleaning
• Reproducible workflow creation (scripts, notebooks)
• Clear presentation of statistical findings

• Fixed monthly fee of 50,000 INR .
• Flexible, output-oriented schedule.
• Initial engagement for 3 months with potential for extension based on performance.

• Design, develop, and implement machine learning algorithms and models.
• Process and analyze large, complex datasets to extract meaningful insights.
• Perform statistical analysis and hypothesis testing.
• Build and maintain data pipelines for model training and deployment.
• Evaluate model performance and iterate for improvement.
• Collaborate with stakeholders to understand business needs and translate them into data science projects.
• Communicate findings and recommendations to technical and non-technical audiences.
• Stay current with the latest advancements in machine learning and data science.

• Master's or Ph.D. in Computer Science, Statistics, Mathematics, or a related quantitative field.
• 3+ years of experience in data science, with a strong focus on machine learning.
• Proficiency in Python or R and relevant libraries (e.g., scikit-learn, TensorFlow, PyTorch).
• Experience with big data technologies (e.g., Spark, Hadoop).
• Strong understanding of statistical modeling and experimental design.
• Excellent problem-solving and analytical skills.
• Experience with cloud platforms (AWS, Azure, GCP) is a plus.
• Strong communication and collaboration skills.

• Design, develop, and implement machine learning models for various applications, including predictive analytics, natural language processing, and computer vision.
• Collect, clean, and preprocess large datasets, ensuring data quality and integrity for model training and evaluation.
• Perform exploratory data analysis to identify trends, patterns, and insights that can inform model development.
• Select appropriate machine learning algorithms and techniques based on the problem domain and data characteristics.
• Train, test, and validate machine learning models, optimizing their performance and accuracy.
• Deploy machine learning models into production environments, working closely with software engineers.
• Monitor model performance in production and implement retraining or updates as necessary.
• Collaborate with product managers and business stakeholders to understand requirements and translate them into data-driven solutions.
• Stay up-to-date with the latest research and advancements in machine learning and artificial intelligence.
• Document methodologies, findings, and model architecture clearly and comprehensively.

• Master's or Ph.D. in Computer Science, Statistics, Mathematics, Data Science, or a related quantitative field.
• Proven experience (3+ years) in applying machine learning techniques to real-world problems.
• Strong proficiency in programming languages such as Python or R, and experience with relevant ML libraries (e.g., TensorFlow, PyTorch, Scikit-learn).
• Solid understanding of statistical concepts, probability theory, and various machine learning algorithms (e.g., regression, classification, clustering, deep learning).
• Experience with data manipulation and analysis tools (e.g., SQL, Pandas).
• Familiarity with big data technologies (e.g., Spark, Hadoop) is a plus.
• Excellent problem-solving and analytical skills.
• Strong communication and collaboration abilities, with the capacity to explain complex technical concepts to non-technical audiences.
• Experience with cloud platforms (AWS, Azure, GCP) is advantageous.

• Design, develop, and implement advanced machine learning algorithms and statistical models to solve complex business problems.
• Extract, clean, and transform large, complex datasets from various sources.
• Perform exploratory data analysis (EDA) to identify trends, patterns, and insights.
• Develop and validate predictive models for forecasting, classification, clustering, and recommendation systems.
• Deploy machine learning models into production environments, collaborating with engineering teams.
• Monitor model performance, retrain models as necessary, and continuously improve their accuracy and efficiency.
• Evaluate and select appropriate machine learning frameworks and tools.
• Communicate complex findings and model insights to technical and non-technical stakeholders through visualizations and presentations.
• Stay current with the latest research and advancements in machine learning, artificial intelligence, and data science.
• Mentor junior data scientists and contribute to the team's knowledge sharing.
• Collaborate with business units to understand their needs and identify opportunities for data-driven solutions.
• Ensure data privacy and security best practices are followed.

• Master's or Ph.D. in Computer Science, Statistics, Mathematics, or a related quantitative field.
• A minimum of 5 years of hands-on experience in data science and machine learning.
• Strong proficiency in programming languages such as Python or R, and relevant libraries (e.g., TensorFlow, PyTorch, scikit-learn).
• Experience with SQL and NoSQL databases.
• Deep understanding of various machine learning algorithms, statistical modeling techniques, and their applications.
• Experience with big data technologies (e.g., Spark, Hadoop) is a plus.
• Proficiency in data visualization tools (e.g., Tableau, Matplotlib).
• Excellent analytical, problem-solving, and critical thinking skills.
• Strong communication and presentation skills, with the ability to explain complex technical concepts to diverse audiences.
• Experience working in a hybrid work environment, with a balance of on-site and remote collaboration.
• Proven ability to translate business requirements into data science solutions.

• Currently pursuing a Bachelor's or Master's degree in Computer Science, Data Science, Statistics, Mathematics, or a related quantitative field.
• Solid understanding of statistical concepts and machine learning algorithms.
• Proficiency in Python or R for data analysis and model development.
• Familiarity with ML libraries (e.g., Scikit-learn, TensorFlow, PyTorch).
• Basic knowledge of SQL and database querying.
• Strong analytical and problem-solving skills.
• Excellent communication and teamwork abilities.
• Eagerness to learn and a proactive attitude.
• Previous project experience or coursework in machine learning is a plus.

• Assist in the collection, cleaning, and preprocessing of large datasets for analysis.
• Develop and implement machine learning models for prediction, classification, and clustering.
• Conduct exploratory data analysis to identify trends and patterns.
• Collaborate with senior data scientists on model evaluation and validation.
• Visualize data and model results to communicate findings effectively.
• Contribute to the development of data pipelines and feature engineering.
• Stay current with the latest research and advancements in data science and machine learning.
• Participate in code reviews and contribute to the team's knowledge base.
• Assist in the deployment and monitoring of machine learning models in production environments.
• Support A/B testing and experimental design.

• Design, develop, and implement sophisticated machine learning models for predictive analytics, pattern recognition, and data-driven decision-making.
• Clean, transform, and preprocess large, complex datasets to prepare them for modeling.
• Perform exploratory data analysis (EDA) to uncover trends, patterns, and anomalies.
• Evaluate and validate model performance, iteratively improving accuracy and robustness.
• Collaborate with software engineers to deploy ML models into production environments.
• Communicate complex analytical findings and insights clearly and concisely to both technical and non-technical stakeholders through visualizations and presentations.
• Stay abreast of the latest advancements in machine learning, deep learning, and data science methodologies.
• Mentor junior data scientists and contribute to the team's technical growth and best practices.
• Identify opportunities to apply ML techniques to solve business problems and enhance product offerings.
• Contribute to the development of data infrastructure and best practices within the team.

• Master's or Ph.D. in Computer Science, Statistics, Mathematics, Data Science, or a related quantitative field.
• 5+ years of professional experience as a Data Scientist or Machine Learning Engineer.
• Proven experience in developing and deploying machine learning models in a production environment.
• Strong expertise in statistical modeling, algorithms (e.g., regression, classification, clustering, deep learning), and data mining.
• Proficiency in Python or R and associated ML libraries (e.g., Scikit-learn, Pandas, NumPy, TensorFlow, PyTorch).
• Experience with SQL and database technologies.
• Familiarity with big data technologies (e.g., Spark, Hadoop) is a plus.
• Excellent analytical, problem-solving, and critical thinking skills.
• Strong communication and presentation skills, with the ability to explain technical concepts to diverse audiences.
• Ability to work independently and collaboratively in a remote team environment.

• Develop and implement advanced machine learning algorithms and statistical models.
• Perform data mining, feature engineering, and exploratory data analysis on large datasets.
• Design and execute experiments to test hypotheses and validate model performance.
• Deploy machine learning models into production environments.
• Collaborate with cross-functional teams to define project requirements and deliverables.
• Communicate complex technical findings and recommendations to stakeholders.
• Stay abreast of the latest research and advancements in AI and machine learning.
• Mentor junior data scientists and contribute to the team's technical growth.
• Optimize model performance and ensure scalability and reliability.

• Master's or Ph.D. in Computer Science, Statistics, Mathematics, or a related quantitative field.
• 5+ years of relevant experience in data science and machine learning.
• Proficiency in programming languages such as Python or R, and ML libraries (e.g., TensorFlow, PyTorch, scikit-learn).
• Strong experience with SQL and data manipulation techniques.
• Experience with cloud platforms (AWS, Azure, GCP) and big data technologies (Spark).
• Excellent problem-solving and analytical skills.
• Strong communication and presentation abilities.