627 Junior Data Scientist jobs in New Delhi

About Company

Infostride is a certified firm serving various Federal, State, Local, and County Departments in primarily IT consulting and Services. We specialize in providing Digital Transformation solutions to our clients, including website development, website redesign, custom application development, Cloud Infrastructure& Security & data analytics. With corporate headquarters in the United States & offshore development center in India, our global presence also includes Dubai and Singapore. We leverage the global delivery model approach to provide best-in-class services for our Govt and Commercial Customers.

PFB the job description for the Data Scientist role for your better understanding along with company details as well.

Timings- 02:30 pm to 11:30 pm IST

Location- Noida (Sec-142)

Mode- Work from the Office (3 Days from the office and 2 Days from Home)

About the Role:

We're looking for an experienced Data Scientist to join our Data Science team with a focus on model implementation and operational excellence. In this role, you will be responsible for taking data science models from development to production, ensuring they perform optimally in real-world healthcare environments, and providing ongoing technical support to maintain model reliability and accuracy.

You will work closely with implementation teams and clients to configure models for specific healthcare settings, diagnose and resolve performance issues, validate data quality throughout the deployment process, and provide expert troubleshooting support for our ML and LLM solutions. This hands-on role requires deep technical expertise in model operationalization and the ability to quickly identify and resolve issues that arise during model configuration and ongoing client operations.

What We're Looking For

• 5+ years of relative industry experience developing, launching, and maintaining ML systems in production at scale.
• 2+ years' experience developing and tuning LLM systems.
• Strong cross-functional communication with technical and non-technical partners.
• Hands-on data engineering experience manipulating data sets, data cleaning, and pipelines with strong coding skills in Python and SQL.
• Strong software development foundations - dedication to high code quality, stable architecture

Key Responsibilities

• Implement and configure statistical and machine learning models in production environments, ensuring optimal performance across diverse healthcare settings.
• Diagnose and troubleshoot model performance issues during configuration, including identifying root causes related to data quality, feature engineering, and model parameters.
• Conduct comprehensive data validation throughout the implementation process to ensure model inputs meet quality standards and flag potential issues before they impact performance.
• Provide ongoing technical support to clients, investigating and resolving model-related issues that arise in production environments.
• Collaborate with implementation teams to configure LLM systems for specific client needs, troubleshooting performance bottlenecks and ensuring reliable deployment.
• Monitor deployed models for performance degradation and implement corrective measures to maintain accuracy and reliability.
• Develop and maintain diagnostic tools and processes to streamline troubleshooting and support workflows.
• Work cross-functionally with customer success, implementation, and engineering teams to ensure seamless model deployment and ongoing support.
• Document troubleshooting procedures and create knowledge base materials to support scaling of implementation and support processes.

• Assist in collecting, cleaning, and pre-processing large datasets.
• Develop and implement statistical models and machine learning algorithms.
• Perform exploratory data analysis to identify trends and patterns.
• Collaborate with senior data scientists on model evaluation and validation.
• Assist in creating data visualizations and reports to communicate findings.
• Contribute to the development and maintenance of data pipelines.
• Stay abreast of new developments and techniques in data science and machine learning.
• Participate in team meetings and contribute to project discussions.
• Support the deployment and monitoring of machine learning models.
• Learn and apply best practices in data science and ethical data usage.

• Bachelor's or Master's degree in Data Science, Computer Science, Statistics, Mathematics, or a related quantitative field.
• Foundational knowledge of machine learning algorithms and statistical modeling.
• Proficiency in programming languages such as Python (with libraries like Pandas, NumPy, Scikit-learn) or R.
• Experience with SQL for data extraction and manipulation.
• Strong analytical and problem-solving abilities.
• Excellent communication skills, with the ability to explain technical concepts clearly.
• Self-motivated and able to work independently in a remote setting.
• Eagerness to learn and adapt to new technologies and methodologies.
• Familiarity with data visualization tools (e.g., Matplotlib, Seaborn, Tableau) is a plus.
• An understanding of cloud platforms (AWS, Azure, GCP) is beneficial.

• Design, develop, and implement machine learning models and algorithms.
• Analyze large, complex datasets to identify trends, patterns, and insights.
• Perform data cleaning, preprocessing, and feature engineering.
• Evaluate and validate model performance using appropriate metrics.
• Deploy machine learning models into production environments.
• Collaborate with business stakeholders to define project requirements and deliver solutions.
• Communicate findings and recommendations clearly to both technical and non-technical audiences.
• Stay current with the latest advancements in data science and machine learning.
• Mentor junior data scientists and contribute to a culture of learning.
• Contribute to the development of data infrastructure and pipelines.

• Master's or Ph.D. in Computer Science, Statistics, Mathematics, or a related quantitative field.
• Minimum of 6 years of experience in data science and machine learning.
• Proven experience in developing and deploying ML models in production.
• Proficiency in programming languages such as Python or R.
• Strong knowledge of ML libraries and frameworks (e.g., TensorFlow, PyTorch, Scikit-learn).
• Experience with big data technologies (e.g., Spark, Hadoop) and SQL.
• Familiarity with cloud platforms (AWS, Azure, GCP) is a plus.
• Excellent analytical, problem-solving, and critical thinking skills.
• Strong communication and presentation abilities.

• Design, develop, and implement advanced machine learning models and algorithms to solve complex scientific problems.
• Work with large, complex datasets from various sources, ensuring data quality and integrity.
• Perform data preprocessing, feature engineering, and selection for model training.
• Train, evaluate, and optimize ML models using appropriate metrics and techniques.
• Deploy machine learning models into production environments and monitor their performance.
• Collaborate with research scientists and engineers to understand data needs and translate scientific requirements into data science solutions.
• Identify new opportunities for applying machine learning and AI to enhance research and development processes.
• Conduct exploratory data analysis (EDA) to uncover trends and patterns.
• Communicate complex findings, methodologies, and insights clearly to both technical and non-technical stakeholders through presentations and reports.
• Stay up-to-date with the latest research and advancements in machine learning, deep learning, and artificial intelligence.
• Contribute to the development of data infrastructure and tools.
• Mentor junior data scientists and contribute to a culture of learning and innovation.
• Ensure ethical considerations and bias mitigation are addressed in model development.

• Master's or Ph.D. in Computer Science, Statistics, Mathematics, Physics, Engineering, or a related quantitative field.
• Minimum of 5 years of hands-on experience in data science and machine learning, with a focus on developing and deploying ML models.
• Proficiency in programming languages such as Python or R, and relevant ML libraries (e.g., TensorFlow, PyTorch, Scikit-learn).
• Strong understanding of statistical modeling, data mining, and machine learning algorithms (e.g., regression, classification, clustering, deep learning).
• Experience with big data technologies (e.g., Spark, Hadoop) and databases (SQL, NoSQL).
• Familiarity with cloud platforms (AWS, Azure, GCP) and MLOps practices.
• Excellent analytical, problem-solving, and critical thinking skills.
• Strong communication and presentation skills, with the ability to explain complex concepts effectively.
• Experience in scientific research or R&D environments is highly preferred.
• Proven ability to work independently and as part of a collaborative research team.

• Lead the design, development, and deployment of sophisticated machine learning models for various business applications.
• Mentor and guide a team of data scientists, providing technical leadership and fostering a collaborative environment.
• Collaborate with cross-functional teams, including engineering, product management, and business stakeholders, to identify opportunities for data science applications.
• Conduct exploratory data analysis to uncover trends, patterns, and insights that can inform business strategy.
• Develop and implement robust data pipelines and MLOps practices for efficient model training, evaluation, and deployment.
• Stay current with the latest advancements in machine learning, artificial intelligence, and statistical modeling techniques.
• Communicate complex findings and model results clearly and concisely to both technical and non-technical audiences.
• Define key performance indicators (KPIs) for model performance and business impact, and track progress over time.
• Contribute to the development of the company's data science strategy and best practices.
• Ensure the ethical and responsible use of data and AI technologies.

• Master's or Ph.D. in Computer Science, Statistics, Mathematics, Data Science, or a related quantitative field.
• Minimum of 7 years of experience in data science and machine learning, with a proven track record of delivering impactful solutions.
• At least 2 years of experience in a leadership or mentoring role.
• Strong proficiency in programming languages such as Python or R, and experience with relevant libraries (e.g., TensorFlow, PyTorch, Scikit-learn).
• Experience with big data technologies (e.g., Spark, Hadoop) and cloud platforms (e.g., AWS, Azure, GCP).
• Solid understanding of statistical modeling, hypothesis testing, and experimental design.
• Excellent problem-solving skills and the ability to translate business problems into data science solutions.
• Strong communication and presentation skills, with the ability to explain complex technical concepts to diverse audiences.
• Experience with data visualization tools (e.g., Tableau, Matplotlib).
• Familiarity with MLOps principles and tools is a plus.

• Assist in the collection, cleaning, and preprocessing of large datasets for machine learning model development.
• Collaborate with senior data scientists to design and implement machine learning algorithms for various applications.
• Develop and test predictive models using programming languages such as Python or R.
• Perform exploratory data analysis to identify trends, patterns, and insights.
• Contribute to the evaluation and tuning of machine learning models to optimize performance.
• Document code, methodologies, and findings clearly and concisely.
• Participate in team meetings, brainstorming sessions, and project reviews in a virtual setting.
• Assist in the visualization of data and model results to communicate findings effectively.
• Learn and apply new data science tools, libraries, and techniques.
• Support ongoing data-related projects and initiatives as needed.
• Gain exposure to the end-to-end machine learning lifecycle.

• Currently pursuing or recently completed a Bachelor's or Master's degree in Computer Science, Data Science, Statistics, Mathematics, Engineering, or a related quantitative field.
• Foundational understanding of machine learning concepts, algorithms, and principles.
• Proficiency in at least one programming language commonly used in data science, such as Python or R.
• Familiarity with data science libraries (e.g., Pandas, NumPy, Scikit-learn, TensorFlow, PyTorch).
• Basic knowledge of SQL and database querying.
• Strong analytical and problem-solving skills.
• Excellent communication and collaboration skills, suitable for remote team participation.
• Eagerness to learn and a proactive attitude.
• Ability to work independently and manage tasks effectively in a remote setting.
• A genuine interest in machine learning and artificial intelligence.

• Assist senior data scientists in data collection, cleaning, and preprocessing from various sources.
• Support the development and implementation of machine learning models under guidance.
• Perform exploratory data analysis to identify patterns and insights.
• Help in building and validating predictive models.
• Document processes, findings, and methodologies clearly and concisely.
• Collaborate with team members through virtual communication channels.
• Learn and apply new data science techniques and tools.
• Assist in the preparation of reports and presentations on project outcomes.
• Contribute to team discussions and problem-solving sessions.
• Ensure data integrity and quality throughout the project lifecycle.

• Bachelor's or Master's degree in Computer Science, Statistics, Mathematics, Data Science, or a related quantitative field.
• Strong foundational knowledge of statistics, probability, and linear algebra.
• Familiarity with programming languages commonly used in data science, such as Python or R.
• Understanding of basic machine learning concepts and algorithms.
• Excellent analytical and problem-solving skills.
• Strong communication and collaboration skills, suitable for a remote team environment.
• Eagerness to learn and adapt to new technologies and methodologies.
• Ability to work independently and manage tasks effectively in a remote setup.
• A proactive approach and a keen interest in data-driven problem-solving.
• Previous personal projects or coursework in data science or machine learning is a plus.

• Design, develop, and deploy sophisticated machine learning models for various scientific applications, including prediction, classification, clustering, and anomaly detection.
• Conduct exploratory data analysis on large, complex datasets to identify patterns, trends, and insights relevant to research objectives.
• Collaborate with research scientists and engineers to understand their data challenges and translate them into data science problems.
• Select appropriate ML algorithms and techniques based on the nature of the data and the problem to be solved.
• Build and maintain data pipelines for efficient data processing and feature engineering.
• Evaluate model performance using robust metrics and iterate on models to improve accuracy and generalization.
• Develop and implement strategies for model validation, testing, and deployment in research environments.
• Stay abreast of the latest advancements in machine learning, artificial intelligence, and data science methodologies.
• Communicate complex analytical findings and model insights clearly and effectively to both technical and non-technical stakeholders through reports and presentations.
• Contribute to the development of research roadmaps and data strategy initiatives.
• Mentor junior data scientists and contribute to the growth of the data science practice within the organization.
• Ensure data quality, integrity, and ethical considerations are maintained throughout the data science lifecycle.
• Explore and integrate new data sources and technologies to enhance analytical capabilities.
• Develop prototypes and proof-of-concepts for new data-driven solutions.
• Document methodologies, code, and results thoroughly.

• Master's or Ph.D. in Computer Science, Statistics, Mathematics, Physics, or a related quantitative field.
• Minimum of 5 years of experience in data science with a strong focus on machine learning applications in a research or development setting.
• Proficiency in programming languages such as Python or R, and extensive experience with ML libraries (e.g., Scikit-learn, TensorFlow, PyTorch).
• Deep understanding of various ML algorithms (e.g., regression, classification, deep learning, reinforcement learning).
• Experience with big data technologies (e.g., Spark, Hadoop) and databases (SQL, NoSQL).
• Strong analytical and problem-solving skills, with a demonstrated ability to tackle complex scientific problems.
• Excellent communication and presentation skills, with the ability to explain technical concepts clearly.
• Experience with data visualization tools (e.g., Matplotlib, Seaborn, Tableau).
• Ability to work independently and collaboratively in a team-oriented environment.
• Must be able to work on-site in Ghaziabad, Uttar Pradesh, IN .