Top Principal Data Scientist Jobs in Boston, MA
As a Principal Data Scientist, you'll collaborate with a diverse team to develop AI-powered products by utilizing advanced technologies. Your responsibilities include leveraging insights from vast data volumes, communicating complex data science concepts into business objectives, and applying state-of-the-art machine learning techniques throughout the model lifecycle.
As a Principal Data Scientist at Capital One, you'll lead research and development of machine learning technologies, build and operationalize models to enhance customer experiences and business outcomes, and collaborate with product and engineering teams across the organization.
As a Principal Data Scientist at Capital One, you will lead a cross-functional team to develop AI-powered products, leveraging advanced technologies like machine learning and NLP to enhance customer experiences. You'll analyze vast amounts of data to inform decision-making and operationalize models in production, ensuring innovative solutions that prioritize customer needs.
The Data Science Co-op at Solaria Labs involves supporting data science efforts related to public health and safety. Responsibilities include applying machine learning techniques, collaborating with engineers and data scientists, developing algorithms, and participating in product design and ideation.
As a Principal Data Scientist II, you will define AI technology and architecture for Seismic, collaborate with teams to develop AI and ML applications, integrate AI capabilities, lead technical teams, and drive product innovation in alignment with business goals.
In this role, you will leverage advanced data science techniques to identify business opportunities, develop and deploy machine learning models, ensure their accuracy, and promote collaboration within a diverse team. You will also engage in hands-on development of scalable solutions and guide best practices in the data science discipline at Kyruus Health.
As a Data Scientist at Flipside Crypto, you will create and enhance web3 data products, collaborating with internal teams and the community. Your responsibilities will include data analysis using SQL databases, leveraging R and/or Python for model-building, and helping product teams transition to data-driven approaches while supporting marketing efforts to communicate findings.
The Outcomes Research Data Scientist will analyze real-world evidence (RWE) data, conduct statistical analyses, and provide strategic recommendations based on research findings. The role involves collaborating with various teams to improve data quality and ensuring compliance with regulations.
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As a Data Scientist II at Klaviyo, you will build models and solutions to improve the product and streamline operations. You'll work closely with stakeholders, applying advanced data science and machine learning techniques, contributing code, and leading projects to enhance customer outcomes.
The Senior Data Scientist will drive innovation in data analysis, predictive modeling, and operational efficiency. Responsibilities include participating in data science projects, communicating findings to stakeholders, and conducting various analyses to optimize dispatch operations and improve customer satisfaction. Key projects may involve developing ETA prediction models and leveraging extensive datasets for insights.
As a Data Scientist Co-Op, you will analyze healthcare data, provide actionable insights, and create technical requirements. Collaborating with experienced data scientists, you will leverage various data sources to assess impacts on costs and quality, while employing data visualization techniques to present findings and solutions.
As a Staff Data Scientist at ezCater, you will lead the design and deployment of data science models to enhance business operations. Responsibilities include mentoring teams, developing predictive models, analyzing large datasets, and guiding A/B testing experiments to improve customer engagement and retention.
As a Data Science Co-op at Jellyfish, you will develop algorithms, contribute to research presentations, learn to create machine learning models, and help analyze and improve software development processes. You will collaborate with a cross-functional team and work with real-world data to generate insights.
The RWE Data Scientist will lead analyses of Tempus data for studies, derive real-world endpoints, interpret results, communicate findings to Pharma teams, collaborate with internal teams, and ensure compliance with regulations. They must have expertise in real-world data analytics and strong project management skills.
As a Senior Analytics Engineer, you will design and maintain data models, collaborate with various teams, ensure data quality, and enhance the analytics platform using tools like dbt and Snowflake. You will also manage data governance and support data-driven decision-making across the organization.
As a Senior Manager of Data Science at Capital One, you will lead a team in building advanced machine learning pipelines for entity resolution, collaborating with cross-functional teams to deliver impactful data solutions. Your role includes developing machine learning models and translating complex data insights into business objectives, while fostering an environment of innovation and continuous learning.
As a Lead Data Science Engineer, you'll lead a team to develop and implement advanced machine learning models and algorithms, collaborate across teams to integrate solutions, and communicate insights to influence business strategies in the Sportsbook industry.
As an Analytics Engineer, you will create data modeling solutions, automate analyses, and collaborate with various stakeholders to improve data-driven processes and enable self-service analytics. You'll ensure data validity and usability while enhancing end-user data literacy through training.
The Senior Manager of Data Science will lead the development of a data science team, translating business challenges into data projects and ensuring effective implementation. This role emphasizes building relationships, coaching team members, and delivering measurable results through innovative AI solutions in the legal tech industry.
As a Data Scientist at Jellyfish, you will collaborate with product teams to design and implement innovative solutions using data analysis, machine learning, and advanced algorithms. You'll focus on enhancing the Jellyfish platform through effective communication, rigorous testing, and continuous improvement of data science models and deployment strategies.
The Data Scientist will build centralized data models and analytical tools to support the engineering and go-to-market functions at Benchling. Responsibilities include partnering with leadership to define analytics strategy, developing KPIs, identifying customer trends, and integrating insights between teams. Requires strong proficiency in data modeling, data visualization, and scripting languages like Python.
As a Decision Analytics Associate Consultant at ZS, you will develop advanced statistical models and AI techniques to help clients understand business issues. You'll leverage data analytics for decision-making, implement AI solutions, and design custom analyses using tools like R, Python, and SQL. You'll also present results, build client relationships, and mentor team members.
The Lead Analytics Engineer will develop and optimize data analytics frameworks, mentor the team, and drive strategy aligned with business needs. Responsibilities include managing ETL processes, implementing data orchestration with Airflow and Dagster, ensuring data accuracy, and leading analytics projects.
The Data Science Manager will lead the development of innovative ML-based enterprise products, manage hybrid teams to solve complex cross-functional problems, engage with clients, and drive AI solutions across sectors. Responsibilities include research, product architecture, and talent development. Extensive experience in building AI/ML products is required.
The Lead Data Engineer will expand and optimize data pipelines and architecture, support data initiatives, engage in data mapping, and assist with data architecture design. They solve complex application issues, validate test plans, and create functional documents while collaborating with multiple teams to enhance data transformation and collection processes.
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