We are a dynamic and rapidly expanding enterprise technology organization operating in a data-driven market where analytics, artificial intelligence, machine learning, customer intelligence, product insight, and predictive decision-making directly influence long-term business performance. Our organization supports enterprise customers, digital platforms, product teams, revenue teams, customer success groups, finance leaders, operations teams, and executive decision-makers who rely on accurate data, strong analysis, and practical insights to guide strategy.
As organizations continue to scale, data science has become one of the most important functions connecting raw information with measurable business value. Companies need data scientists who can do more than build models or generate reports. They need professionals who can understand business problems, identify useful patterns, build predictive solutions, test assumptions, communicate findings clearly, and help teams make better decisions with confidence.
Our company is investing heavily in AI-enabled analytics, machine learning models, cloud data infrastructure, product intelligence, customer segmentation, forecasting, experimentation, automation, data governance, predictive risk analysis, and enterprise reporting modernization. We are seeking a highly capable Data Scientist who can turn complex data into clear insight, build practical models, and support strategic decisions across a fast-growing remote-first organization.
This is not a traditional data science role focused only on isolated analysis, dashboard requests, or academic modeling. The Data Scientist will serve as a practical analytical partner responsible for developing models, analyzing customer and business behavior, improving decision support, supporting product and growth initiatives, and translating technical findings into recommendations that business leaders can use.
The selected candidate will work closely with Data Engineering, Machine Learning Engineering, Product, Business Intelligence, Finance, Revenue Operations, Customer Success, Marketing, Operations, Security, and Executive Leadership to ensure data science work is aligned with business priorities, technically sound, measurable, and actionable.
This is a strong opportunity for a data science professional who wants to work with modern data platforms, support AI-enabled business transformation, influence product and customer strategy, and help build a more intelligent, data-driven organization.
As Data Scientist, you will analyze complex datasets, build predictive models, develop statistical insights, support experimentation, and create data-driven recommendations that improve business outcomes.
You will be responsible for identifying patterns, designing analyses, preparing datasets, validating results, building models, communicating findings, and partnering with business teams to turn insight into action.
You will serve as a trusted analytical partner while maintaining accountability for data accuracy, model quality, business relevance, documentation, responsible AI practices, and practical decision support.
The ideal candidate combines strong statistical knowledge, machine learning capability, business curiosity, technical fluency, communication skill, and sound judgment to support measurable enterprise results.
Analyze large and complex datasets to identify patterns, trends, risks, opportunities, customer behaviors, product usage signals, financial drivers, and operational performance insights.
Build predictive models, classification models, forecasting models, segmentation frameworks, recommendation logic, churn models, propensity models, anomaly detection models, and other machine learning solutions.
Partner with business stakeholders to understand goals, define analytical questions, translate problems into data science approaches, and deliver practical recommendations.
Partner with Data Engineering to ensure datasets are reliable, well-structured, properly documented, and ready for analysis, modeling, and reporting.
Partner with Product and Engineering teams to support product analytics, feature evaluation, experimentation, usage analysis, customer behavior research, and AI-enabled product capabilities.
Design and evaluate experiments, A/B tests, causal analyses, statistical studies, and measurement frameworks to assess product, marketing, operational, or customer initiatives.
Develop dashboards, reports, analytical summaries, model outputs, and executive-ready presentations that explain insights clearly and support decision-making.
Use SQL, Python, R, notebooks, cloud data platforms, visualization tools, and statistical packages to analyze data and develop scalable analytical workflows.
Clean, transform, validate, and prepare data for analysis while identifying quality issues, missing values, inconsistent definitions, and data reliability risks.
Evaluate model performance, monitor accuracy, identify bias or drift, document assumptions, and support responsible AI and model governance practices.
Communicate complex findings in clear business language, including methodology, confidence level, limitations, risks, and recommended next steps.
Support customer intelligence initiatives such as segmentation, retention analysis, lifetime value modeling, journey analytics, satisfaction drivers, and expansion opportunity identification.
Support operational and financial analysis related to forecasting, productivity, cost trends, risk detection, resource planning, and business performance improvement.
Contribute to data science best practices, reusable analytical frameworks, documentation standards, model review processes, and continuous improvement of data science workflows.
5+ years of experience in data science, machine learning, statistical analysis, predictive modeling, product analytics, business analytics, decision science, or applied AI.
Experience working within enterprise SaaS, AI-enabled technology, cloud platforms, fintech, healthcare technology, cybersecurity, ecommerce, customer experience technology, or large-scale data environments preferred.
Strong programming experience with Python, R, SQL, or similar tools used for data analysis, modeling, automation, and statistical workflows.
Experience building machine learning models using tools and libraries such as scikit-learn, TensorFlow, PyTorch, XGBoost, LightGBM, pandas, NumPy, SciPy, Statsmodels, or similar technologies.
Strong understanding of statistics, probability, regression, classification, clustering, forecasting, hypothesis testing, experimental design, causal inference, model validation, and performance measurement.
Experience working with cloud data platforms such as Snowflake, Databricks, BigQuery, Redshift, Azure Synapse, PostgreSQL, or similar environments.
Experience using visualization and business intelligence tools such as Tableau, Power BI, Looker, Mode, Sigma, Qlik, matplotlib, Plotly, or similar platforms.
Experience with product analytics, customer analytics, marketing analytics, financial analytics, risk analytics, operational analytics, or revenue analytics preferred.
Familiarity with data engineering concepts, ETL and ELT workflows, data modeling, feature engineering, APIs, event tracking, data governance, and model deployment patterns preferred.
Ability to work with Data Engineering, Machine Learning Engineering, Product, Business Intelligence, Finance, Revenue Operations, Marketing, Customer Success, Operations, Security, and leadership teams.
Strong communication skills with the ability to explain technical findings to non-technical audiences and connect analysis to business decisions.
Bachelor’s degree in Data Science, Statistics, Mathematics, Computer Science, Economics, Engineering, Information Systems, Analytics, or a related quantitative field required.
Master’s degree, PhD, machine learning certification, cloud data certification, AI training, or advanced analytics education preferred.
Competitive compensation package.
Comprehensive medical, dental, and vision healthcare coverage.
Flexible remote-first work environment.
Performance bonus eligibility.
Data science, analytics, and AI impact incentive opportunities.
Long-term incentive opportunities where applicable.
Retirement savings plan with company contribution.
Professional development and continuing education reimbursement.
Data science, machine learning, AI ethics, cloud analytics, predictive modeling, experimentation, product analytics, and technical leadership development resources.
Wellness and mental health support programs.
Paid time off and company holidays.
Opportunity to work on enterprise-level AI initiatives, predictive analytics, customer intelligence, product analytics, forecasting, and digital transformation programs.
Access to modern cloud data platforms, machine learning tools, BI platforms, collaboration systems, experimentation frameworks, and AI-enabled analytics resources.
Strong analytical thinker with the ability to turn complex data into clear, practical business insight.
Technically skilled data scientist with strong command of statistics, machine learning, data preparation, model evaluation, and analytical storytelling.
Business-minded and curious, with the ability to understand company priorities, ask strong questions, and focus analysis on decisions that matter.
Detail-oriented and disciplined, with strong ownership of data quality, model assumptions, documentation, validation, and analytical accuracy.
Clear communicator who can explain methods, findings, confidence levels, limitations, and recommendations in a way that business leaders can understand.
Collaborative and able to work effectively with data engineers, product managers, business analysts, machine learning engineers, finance partners, customer success teams, and executives.
Problem-solver with the ability to break ambiguous business questions into structured analytical plans.
Responsible and ethical in the use of data, with awareness of privacy, bias, fairness, explainability, and model governance.
Improvement-minded, with interest in reusable frameworks, automation, better documentation, and stronger analytical processes.
High integrity and discretion when handling customer data, financial information, product usage data, employee data, confidential business metrics, and model outputs.
Support executive leadership with predictive insights, statistical analysis, forecasting, customer intelligence, product performance analysis, and decision support.
Help improve business performance by identifying drivers of growth, retention, efficiency, risk, customer satisfaction, and operational quality.
Support AI and machine learning initiatives by developing models, preparing features, validating outcomes, monitoring performance, and supporting responsible AI practices.
Align data science work with business priorities, customer needs, product strategy, revenue goals, operational performance, and enterprise growth objectives.
Strengthen product strategy by analyzing usage behavior, feature adoption, customer segments, experimentation results, and product value signals.
Support customer success and revenue teams with churn analysis, expansion opportunity models, customer health indicators, retention insights, and segmentation strategies.
Contribute to financial and operational planning through forecasting, scenario analysis, trend detection, productivity analysis, and performance measurement.
Partner with Data Engineering and Business Intelligence teams to improve datasets, metric definitions, dashboard reliability, and analytical readiness.
Support data governance and model governance by documenting assumptions, definitions, methods, limitations, and performance expectations.
Help turn data science into a strategic capability that improves decisions, strengthens customer value, and supports long-term enterprise growth.
Remote-first data science role.
Periodic travel may be required for data strategy sessions, analytics workshops, product planning meetings, executive reviews, company gatherings, or strategic technology sessions.
High-visibility analytical role supporting AI initiatives, business intelligence, product analytics, customer strategy, forecasting, and executive decision-making.
Fast-paced environment focused on data accuracy, AI-enabled innovation, product growth, customer outcomes, operational excellence, and measurable business value.
Regular collaboration with Data Engineering, Machine Learning Engineering, Product, Business Intelligence, Finance, Revenue Operations, Marketing, Customer Success, Operations, Security, and leadership teams.
Opportunity to influence customer intelligence, product direction, model development, forecasting quality, experimentation practices, and business performance.
Requires flexibility during business reviews, data quality issues, model investigations, urgent analysis requests, product launches, reporting deadlines, and leadership planning cycles.
Role requires handling confidential customer data, product usage information, financial data, business performance metrics, employee data, model outputs, and internal strategy documents with discretion.
Data Science.
Machine Learning.
Predictive Modeling.
Statistical Analysis.
Product Analytics.
Customer Analytics.
Business Intelligence Support.
Forecasting.
Experimentation.
AI Analytics.
Data Visualization.
Model Evaluation.
Feature Engineering.
Decision Science.
Remote Data Science.
Compensation Package: $200,000 – $329,000
Base Salary: $200,000 – $329,000.
Annual Performance Bonus.
Data Science Impact Incentives.
AI and Analytics Performance Incentives.
Long-Term Incentive Eligibility where applicable.
Additional benefits may include:
• Comprehensive healthcare coverage.
• Retirement savings plan with company contribution.
• Wellness and mental health programs.
• Professional education assistance.
• Flexible remote work environment.
• Paid time off and company holidays.
• Data science, machine learning, AI ethics, experimentation, product analytics, cloud analytics, predictive modeling, and technical leadership training.
• Access to modern cloud data platforms, machine learning libraries, BI platforms, experimentation tools, collaboration systems, and AI-enabled analytics resources.
We are committed to fostering a workplace where innovation, diversity, and inclusion drive meaningful business outcomes. We provide equal employment opportunities to all applicants and employees regardless of race, religion, gender, age, disability, veteran status, sexual orientation, or any other protected characteristic under applicable law.
Use data science to influence product strategy, customer intelligence, forecasting, AI initiatives, and enterprise decision-making.
Work with modern cloud data platforms, machine learning tools, experimentation frameworks, and AI-enabled analytics workflows.
Partner with product, data, engineering, finance, revenue, and customer-facing teams on meaningful business questions.
Contribute to a remote-first organization investing heavily in AI, predictive analytics, data infrastructure, automation, and digital transformation.
Build models and insights that improve customer outcomes, product adoption, operational performance, and business growth.
Join a company where data science is valued as a strategic function that turns complex information into measurable enterprise value.
At Admiral Talent, our vision is to redefine staffing through genuine partnerships, innovation, and a people-first mindset. Guided by experienced leadership, we’re committed to driving growth, empowering talent, and delivering exceptional results for every client we serve.