The Decision Scientist will provide analytical and statistical support to business teams by transforming complex data into meaningful insights and actionable recommendations.
Locations (2)
Brazil
Mexico
Responsibilities
Apply statistical, mathematical, and quantitative techniques to solve business problems and support strategic decision-making
Conduct exploratory data analysis (EDA) to identify patterns, trends, correlations, outliers, and business opportunities
Develop descriptive and explanatory models to investigate business challenges and support data-driven recommendations
Apply statistical methodologies such as distribution analysis, correlation analysis, multivariate analysis, time series, hypothesis testing, and machine learning
Collect, integrate, clean, and transform data from multiple sources to create reliable datasets for analysis
Requirements
Bachelor's degree in Data Science, Statistics, Mathematics, Business Analytics, Computer Science, or a related field
Lead Data Scientist responsible for defining and executing the analytics and predictive data roadmap for SmartRent’s next-generation offerings, including model development, deployment, and operational support within a Databricks environment.
Responsibilities
Frame business problems with Product and Revenue partners and turn them into modeling approaches with clear success metrics.
Create compelling data visualizations that support insightful storytelling and inform the design of predictive models.
Build machine-learning models on large-scale time-series telemetry, including anomaly detection, failure prediction, and time-to-event models.
Develop clean, well-documented code that promotes reuse and drives operational efficiency within the organization.
Ship models to production on Databricks, and support their quality after launch: monitoring, diagnosing drift, and retraining.
Requirements
Bachelor's degree in Computer Science, Mathematics, Statistics, Engineering, or a related quantitative field; or equivalent professional experience.
Nice to have: LLMs
Nice to have: self-service data access in Databricks
As a Data Scientist at Capital One, you’ll be part of a team that’s leading the next wave of disruption at a whole new scale, using the latest in computing and machine learning technologies and operating across billions of customer records to unlock the big opportunities that help everyday people save money, time and agony in their financial lives. In Capital One’s Model Risk Office, we defend the company against model failures and find new ways of making better decisions with models.
Locations (4)
Chicago, Illinois, United States
McLean, Virginia, United States
Richmond, Virginia, United States
Riverwoods, Illinois, United States
Compensation
Chicago, IL: $123,300 - $140,700 for Sr Assoc, Data Science
McLean, VA: $135,600 - $154,800 for Sr Assoc, Data Science
Richmond, VA: $123,300 - $140,700 for Sr Assoc, Data Science
Riverwoods, IL: $123,300 - $140,700 for Sr Assoc, Data Science
Senior Data Scientist – Build the AI That Powers Life-Saving Medicine Production
Senior Data Scientist in the Data, Analytics & AI team, part of Strategy & Performance, responsible for designing and building end-to-end AI solutions in pharmaceutical manufacturing and supply.
Responsibilities
Design and build AI solutions end to end
Develop LLM and agentic AI applications
Frame the problem before the model
Take solutions into production using MLOps/LLMOps practices
Make AI trustworthy ensuring GxP, data integrity and regulatory compliance
Requirements
Master’s degree (or equivalent experience) in mathematics, statistics, computer science, engineering, physics, operations research or a related quantitative field
Data Scientist who can operate at the intersection of classical machine learning, large-scale recommendation systems, and modern agentic AI systems, designing, building, and deploying intelligent systems that power Glance’s personalized lock screen and live entertainment experiences.
Responsibilities
Design and develop large-scale recommendation systems using advanced ML, statistical modeling, ranking algorithms, and deep learning.
Build and operate machine learning models on diverse, high-volume data sources for personalization, prediction, and content understanding.
Develop rapid experimentation workflows to validate hypotheses and measure real-world business impact.
Own data preparation, model training, evaluation, and deployment pipelines in collaboration with engineering counterparts.
Monitor ML model performance using statistical techniques; identify drifts, failure modes, and improvement opportunities.
The US Brand Analytics Director is the brand’s expert and strategic authority on commercial data and analytics, providing an end-to-end view of performance across patient, HCP, account, access, field and market dynamics to support US and Global decision-making.
Compensation
The annual base pay (or hourly rate of compensation) for this position ranges from $155,516.80 to $233,,275.20. Our positions offer eligibility for various incentives—an opportunity to receive short-term incentive bonuses, equity-based awards for salaried roles and commissions for sales roles.
Responsibilities
Develop a deep understanding of internal customers’ data and analytical needs, with a focus on customer-facing model leadership.
Design and implement analytical solutions for business opportunities, customer segmentation and targeting, HCP and patient opportunity identification, return-on-investment analytics, optimal resource and budget allocation across Sales and Marketing channels, patient and prospective analytics, smart business experiments (test and learn), and market simulation and patient flow modelling.
Coordinate the procurement of syndicated and custom, primary and secondary datasets.
Champion the use of local customer insight in strategic and resourcing decisions across the business.
Collaborate with Commercial, Sales Force Operations and Market Access teams on key business processes and joint initiatives.
Requirements
Quantitative Bachelor’s degree from an accredited college or university in Engineering, Operations Research, Management Science, Economics, Statistics, Applied Math, Computer Science, Data Science or a related field
Lead commercial analytics for the US brand, driving data‑driven insights, AI and automation to inform strategy and decision‑making.
Compensation
The annual base pay (or hourly rate of compensation) for this position ranges from $155,516.80 to $233,,275.20. Our positions offer eligibility for various incentives—an opportunity to receive short-term incentive bonuses, equity-based awards for salaried roles and commissions for sales roles.
Responsibilities
Develop deep understanding of internal customers’ data and analytical needs
Design and implement analytical solutions for segmentation, targeting, ROI, resource allocation, patient flow modelling
Coordinate procurement of syndicated and custom datasets
Champion use of local customer insight in strategic decisions
Collaborate with Commercial, Sales Force Operations and Market Access teams
Requirements
Bachelor’s degree in Engineering, Operations Research, Management Science, Economics, Statistics, Applied Math, Computer Science, Data Science or related field
The DDSI team sits at the intersection of business strategy, advanced analytics, and technology integration to help our partners explore opportunities for analytics, shape business decisions, and drive value.
Compensation
$80-$88/hr
Responsibilities
Achieve business goals and objectives—Research and develop decision science models and act as a consultant to all business units
Pursue innovation—Conduct research on analytical techniques and translate that research into usable and sustainable solutions for users
Put your skills to the test—Perform data collection and data mining, and build state-of-the-art decision science algorithms, tools, and systems
Drive value—Model and analyze revenue management and pricing related issues using various mathematical, statistical, and simulation techniques
Tell the story—Present science results to business partners and clients
Requirements
Master's or Ph.D. degree in Statistics, Operations Research, Industrial Engineering, Mathematics, Machine Learning, Econometrics, or related field
Lead the design and implementation of the algorithmic trading platform, integrating quantitative research, data analytics, and client solutions across various functions within eTrading.
Responsibilities
Lead the QR eTrading team locally to drive algo execution product research and development
Partner closely with the product team and trading desks to design and build client centric solutions
Collaborate with quant researchers and trading desks to refine models and strategies that enhance our algorithm performance
Build robust algorithms on cutting-edge execution platform by collaborating closely with our technology partners to integrate quantitative models and deliver optimal solutions within the algo trading engine
Support diagnosis of trading decisions by explaining model and algorithm behavior, conducting scenario analyses, and developing quantitative tools and data analytics
Requirements
Masters in STEM subjects such as computer science, engineering, mathematics/statistics, physics, operations research
Nice to have: Python
Nice to have: AWS
Nice to have: database/data processing technologies
Lead data science initiatives for consumer credit risk models, deploying and optimizing machine learning pipelines to support strategic decisions at Capital One.
Compensation
McLean, VA: $135,600 - $154,800 for Sr Assoc, Data Science
Responsibilities
Partner with cross-functional teams to deliver data science products
Leverage Python, AWS, Spark to analyze large datasets
Optimize and deploy machine learning models through all development phases
Translate complex data insights into business goals
Requirements
Bachelor's Degree in a quantitative field (Statistics, Economics, Operations Research, Analytics, Mathematics, Computer Science, or related)
Master's Degree in a quantitative field or MBA with quantitative concentration
Partner with a cross-functional team of data scientists, software engineers, and product managers to deliver a product customers love
Leverage a broad stack of technologies — Python, Conda, AWS, H2O, Spark, and more — to reveal the insights hidden within huge volumes of numeric and textual data
Build machine learning models through all phases of development, from design through training, evaluation, validation, and implementation
Flex your interpersonal skills to translate the complexity of your work into tangible business goals
Requirements
Bachelor's Degree in a quantitative field (Statistics, Economics, Operations Research, Analytics, Mathematics, Computer Science, or a related quantitative field) plus 2 years of experience performing data analytics
Master's Degree in a quantitative field (Statistics, Economics, Operations Research, Analytics, Mathematics, Computer Science, or a related quantitative field)
MBA with a quantitative concentration
Master’s Degree in “STEM” field (Science, Technology, Engineering, or Mathematics)
PhD in “STEM” field (Science, Technology, Engineering, or Mathematics)
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