• The Senior Manager, Data Science leads a specialized team of data professionals varying in size and complexity that are responsible for aiding to drive changes and improvement in business practices through data science. • This role manages the overall data scientist team or function for a key business which may include Modelers and/or Data Scientist roles. • This role may also oversee the development of data models, data mining and analytic solutions. • Provides people management leadership by hiring the best talent, setting goals, developing staff, managing employee performance and compensation decisions. • Promotes teamwork and handling any/all disciplinary actions, as required. • Oversees and leads a large and/or highly complex and diverse analytical function for an area of significant risk, complexity or scope. • Strategic partner to leadership team on the management of the portfolio and financials, with deep industry, external/internal, enterprise knowledge, recognizing and anticipating emerging trends and identifying operational efficiencies and opportunities with other business management/enterprise areas. • Facilitates key strategic discussions and provides thought leadership to executive audience.

• Design the Future of Ads Identity: Develop/employ probabilistic models for identity resolution. Design the methodology that links on-platform and off-platform actions to maximize addressability while honoring privacy. • Advance Lift Methodologies & Experimentation: Own the statistical rigor behind Reddit’s Brand and Conversion Lift products. Innovate experimental design and develop infrastructure that supports large-scale, high-velocity, low-bias testing for advertisers. • Maximize Signal for Predictive Performance: Define the strategy for new signal sources. You will mathematically quantify the value of these signals and work with modeling teams to incorporate them into predictive models, directly improving bidding efficiency and ROAS. • Define Ground Truth & Evaluation Frameworks: Solve the industry-wide challenge of validating identity and measurement. Design the objective functions and truth sets used to train our models and measure the incremental impact of our identity graph. • Lead Through Cross-Functional and Technical Influence: Collaborate deeply with engineering, product, and sales to align on strategic goals, translate insights into action, and drive execution. Set a high technical bar by mentoring others and championing best practices across modeling, experimentation, and measurement.

• Define the long-term data science vision & strategy across ads measurement, signal quality, identity, attribution, and privacy. Establish how Reddit should evaluate advertiser value, measurement quality, and signal utility across first-party and third-party products. • Define the long-term data science strategy across ads measurement, signal quality, identity, attribution, and privacy. Establish how Reddit should evaluate advertiser value, measurement quality, and signal utility across first-party and third-party products. • Create rigorous frameworks for validating lift, attribution, identity quality, modeled conversions, signal loss recovery, and privacy-aware measurement. Define ground truth, objective functions, quality metrics, guardrails, and decision frameworks that guide product and engineering investments. • Lead the evolution of Reddit’s experimentation and lift methodologies across Brand Lift, Conversion Lift, Split Testing, and emerging measurement products. Improve study quality, reduce bias and contamination, and develop scalable diagnostics for experiment health, feasibility, and interpretability. Partner with Ads Engineering to operationalize complex causal models, ensuring that scientific methodologies are not only accurate but also performant, scalable, and resilient in high-throughput production environments. • Quantify how signal quality, match rates, identity resolution, modeled conversions, and privacy changes affect bidding efficiency, CPA, ROAS, and advertiser outcomes. Partner with modeling and ranking teams to translate measurement improvements into performance gains. • Lead a privacy-first measurement paradigm, positioning Reddit as a market leader in trusted advertising by recovering signal utility through compliant modeling. • Lead cross-org efforts to define reusable methodologies, dashboards, scorecards, quality metrics, and best practices. Build repeatable systems that improve how Ads DS evaluates launches, monitors regressions, sizes opportunities, and communicates impact. • Partner with senior leaders across Product, Engineering, Sales, Marketing Science, Legal/Privacy, and Ads leadership to shape roadmap decisions. Translate complex scientific tradeoffs into clear business and product recommendations. • Mentor Staff and Senior data scientists, sponsor high-impact technical work, and raise the bar for causal inference, measurement science, identity evaluation, data quality, and cross-functional decision-making across Ads DS.

Role Description We are seeking a highly motivated Data Scientist Intern - Finance - to join our Finance team in a remote capacity. This internship is ideal for a student who has completed at least two years of undergraduate studies and possesses a strong foundation in data analytics, statistics, and finance. The successful candidate will partner with finance and operational leaders to develop predictive models, automate financial analyses, and generate insights that support decision-making within a manufacturing environment. This role offers a unique opportunity to apply advanced data science techniques to real-world financial challenges, including: - Forecasting - Cost analysis - Profitability modeling - Working capital optimization - Operational performance measurement Duties and Responsibilities - Develop, validate, and maintain data science and machine learning models supporting finance and manufacturing operations. - Build predictive models for: - Revenue forecasting - Demand planning - Cost and margin analysis - Working capital management - Cash flow forecasting - Inventory optimization - Analyze large datasets from ERP, financial, operational, and manufacturing systems. - Create dashboards, visualizations, and reports that communicate complex analyses to finance and business stakeholders. - Assist in identifying key business drivers and recommend opportunities to improve financial and operational performance. - Automate recurring financial reporting and analytical processes. - Collaborate with Finance, Supply Chain, Operations, and IT teams to gather requirements and deliver impactful analytical solutions. - Conduct statistical analyses and data quality assessments to ensure data integrity and model accuracy. - Present findings and recommendations to executive leadership and cross-functional teams. Qualifications - Currently enrolled in a Bachelor's degree program in: - Data Science - Statistics - Mathematics - Computer Science - Finance - Related quantitative field - Completion of at least two (2) years of undergraduate coursework. - Minimum cumulative GPA of 3.0 preferred. - Strong understanding of financial concepts, including: - Income statements - Balance sheets - Cash flow statements - Budgeting and forecasting - Financial performance metrics - Proficiency with Python and data analysis libraries (Pandas, NumPy, Scikit-learn). - Experience with SQL and data extraction techniques. - Knowledge of statistical modeling, predictive analytics, and machine learning concepts. - Strong analytical, problem-solving, and critical thinking skills. - Excellent written and verbal communication skills. - Ability to work independently in a remote environment while managing multiple priorities. Preferred Qualifications - Coursework or internship experience in corporate finance, financial analysis, or manufacturing analytics. - Experience with business intelligence tools such as Power BI, Tableau, or similar platforms. - Familiarity with ERP systems (SAP, Oracle, Microsoft Dynamics, or similar). - Knowledge of forecasting techniques, time-series modeling, and optimization methods. - Exposure to cloud-based analytics platforms (Azure, AWS, or Google Cloud). - Understanding of manufacturing KPIs and cost accounting principles. Learning Opportunities - Financial forecasting and strategic planning - Manufacturing finance and cost analysis - Advanced data science and machine learning applications - Financial modeling and business analytics - Executive-level reporting and decision support - Cross-functional collaboration within a manufacturing organization Competencies - Financial Acumen - Data-Driven Decision Making - Business Partnership - Attention to Detail - Analytical Thinking - Intellectual Curiosity - Adaptability - Collaboration - Results Orientation