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Top Data Science Jobs in India
As a Senior Analyst in Data Science, you will be responsible for implementing analytical solutions, extracting and analyzing data from various sources, creating data pipelines, and applying MLOps frameworks for real-time monitoring of AI/ML models. Collaborating with global teams, you will ensure timely deliveries and develop insights from data.
The Data Engineer II role involves building software for a data platform, developing and maintaining data integrations, ingesting and analyzing campaign datasets, ensuring data quality, and collaborating with various teams, including data architects and AI/ML teams.
Join as a Senior Advisor, Machine Learning Engineer specializing in LLMOps at Dell Technologies in India. Architect, build, and deploy sophisticated General/Generative AI solutions with a focus on operational efficiency and scalability. Work on innovative projects leveraging massive datasets and state-of-the-art language models to drive decision-making and operational efficiencies globally.
As a Data Scientist Lead at JPMorgan Chase, you will design and manage language models for various NLP tasks in financial services. Responsibilities include conducting research, collaborating with teams, building data pipelines, and analyzing model performance. Your expertise will help drive innovation in wealth management through advanced analytics.
Lead a team of data scientists in developing advanced data science and generative AI solutions. Oversee data strategy, algorithm development, and model creation while analyzing healthcare data to generate insights and drive innovative strategies, working closely with stakeholders.
As a Senior Data Scientist at FourKites, you will tackle complex supply chain challenges using advanced analytics and machine learning. You'll bridge data science and engineering teams, optimize ML models, develop scalable AI products, and create ad hoc reports. You'll also identify data quality issues and define strategies for ML-driven product development.
The role involves developing and optimizing machine learning models and AI algorithms to address complex business issues. Responsibilities include designing and fine-tuning AI models, deploying them on cloud platforms, collaborating with clients, mentoring junior team members, and staying updated on industry trends.
As a Manager Data Science 2, you will lead a team focused on optimizing payment solutions for small and medium businesses at PayPal. Responsibilities include data analysis, managing a team, and presenting strategic insights to influence product improvement using complex statistical models and experimentation.
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The Senior Data Scientist will collaborate with the Marketing team to enhance marketing ROI by leveraging statistical modeling, experimentation, and data visualization. Responsibilities include analyzing marketing performance, developing dashboards, and engaging in customer segmentation to inform strategic decisions.
As a Data Scientist in Payments Data & Analytics, you will analyze how money flows globally, creating insights that impact product strategy. Responsibilities include developing analytic projects, creating visualizations, and collaborating with stakeholders to enhance data models and algorithms for business objectives.
The Senior Data Scientist will develop innovative data science solutions to address complex business problems, deliver end-to-end solutions using advanced ML algorithms on large datasets, and communicate effectively with diverse audiences. The role involves mentorship of junior staff and adherence to ethical practices.
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The Lead - Data Science will supervise model validations, mentor junior team members, enhance model performance by analyzing complex datasets using Python and BigQuery on Google Cloud, and manage model risks. Additionally, they will contribute to model testing and deliver insights to stakeholders.
The manager of Data Science will lead AI-driven initiatives, develop predictive models, and analyze complex datasets to inform business decisions. They will mentor a team of data scientists, manage stakeholder communications, and promote innovation in data science techniques for effective business solutions.
The Manager of Data Science will explore and implement digital innovation solutions addressing customer needs while leading projects across various marketing channels. This role involves planning and managing project timelines, ensuring quality deliverables, and collaborating with brand and technical teams for successful execution. The manager will contribute to strategic decision-making using proven analytics and provide guidance on commercial processes.
The Senior Data Scientist is responsible for designing and developing methods for analyzing large, unstructured data sets. The role includes programming automated processes to clean and integrate diverse data sources, and communicating actionable insights to stakeholders.
The Staff Data Scientist will develop user lifecycle and segmentation models, create Tableau dashboards, build data pipelines, and collaborate with stakeholders to drive data-driven decision-making. This role requires strong analytical problem-solving skills and the ability to present insights to both technical and non-technical audiences.
The Senior Manager - Data Science will lead data science initiatives, apply advanced methodologies and machine learning techniques, collaborate with cross-functional teams, conduct analysis on large datasets, mentor junior members, and present findings to stakeholders to drive business decisions at Razorpay.
As a Data Engineer, you will collaborate with business stakeholders, perform data curation using PySpark and Airflow, develop Tableau dashboards, and work with Hive in Big Data environments. You will bring robust programming and SQL skills, particularly with Snowflake, to enhance data workflows and support organizational data needs.
As a Data Scientist III, you will develop statistical models and machine learning algorithms to drive insights across retail divisions. Your role includes data gathering, assessing validity, synthesizing analytics datasets, productionizing models, and communicating findings to influence business strategy.
As a Trainee Data Scientist at Equifax, you will develop and implement statistical models and machine learning algorithms to analyze credit risk and customer behavior. Collaborate with teams to translate business requirements into actionable insights, perform exploratory data analysis, and ensure data quality. Stay updated on data science advancements and apply them to solve business problems.
As a Data Science Engineer, you will develop and maintain tools to enhance data science development. Your role includes optimizing data pipelines, collaborating with infrastructure teams, and working on cloud platforms. You will also promote better working methods in distributed computing and develop web applications using various technologies.
As a Trainee Data Scientist at Equifax, you'll develop and implement statistical models and machine learning algorithms while collaborating with teams to deliver actionable data insights for clients. Responsibilities include exploratory data analysis, ensuring data quality, and staying updated on data science advancements.
As a Manager in Data Science, you'll lead the development of innovative data science solutions using ML Ops, mentor a high-performance team, and collaborate with various stakeholders to drive data excellence and business impact.
As a Trainee Data Scientist at Equifax, you will develop and implement statistical models and machine learning algorithms to predict credit risk and customer behavior. You'll collaborate with teams to translate business requirements into data insights, ensuring data quality through rigorous cleaning and validation processes while staying updated on advancements in data science.
As a Trainee Data Scientist at Equifax, you will develop statistical models and machine learning algorithms to enhance Retail Bank Marketing and Risk Analytics. Key responsibilities include data analysis, feature engineering, ensuring data integrity, and collaborating with teams to deliver actionable insights. Staying updated with data science advancements is also crucial.
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