Top Software Data Science Jobs

Leads a data and analytics team responsible for AI models, reporting, trusted datasets, pipelines, and decision-support products. Manages analytics intake, prioritization, delivery standards, model deployment and monitoring, data quality, governance, and stakeholder communication. Oversees hiring, coaching, performance management, vendor tooling, and roadmap execution while ensuring responsible data and AI practices.

Owns marketing and growth measurement, including attribution, incrementality, CAC, LTV, ROAS, experimentation, and budget optimization. Builds predictive models, data models, dashboards, reporting, and lightweight pipelines using product, advertising, behavioral, and on-chain data. Partners with Growth, Marketing, Product, and Engineering to improve acquisition performance, data infrastructure, and user-value understanding.

The Forward Deployed Engineer partners with enterprise clients to identify AI opportunities and deliver production-ready solutions. Responsibilities include building forecasting, optimization, clustering, anomaly detection, generative AI, and agent-based systems; defining data and modeling strategies; implementing monitoring, retraining, and governance; and supporting organizational adoption. The role contributes to multiple implementations while collaborating with BI, Finance, Operations, and business stakeholders.

Develop descriptive, predictive, and causal analytics for local government datasets. Responsibilities include maintaining data pipelines, cleaning and refreshing databases, weighting surveys, building statistical and machine learning models, conducting impact studies, automating cloud workflows, presenting insights, and documenting reproducible processes. The role partners with engineering, marketing, sales, customer success, and survey research teams, and requires Python, SQL, statistics, communication, and applied data science experience.

Build and maintain factory data pipelines, structure production datasets, analyze manufacturing performance, develop dashboards and metrics, and prototype statistical or machine learning solutions. Collaborate with operations, quality, engineering, and program management teams to turn business questions into measurable analyses. Contribute clean, tested, documented code, identify data quality issues, and help govern factory data.

Develop and improve analytical and machine learning models supporting product quality, risk outcomes, and business decisions. Responsibilities include statistical analysis, feature development, data exploration, predictive modeling, SQL-based analysis of large datasets, algorithm research, and workflow documentation. The role collaborates with data scientists, product managers, data engineers, and analysts in a product-focused fintech environment.

Analyzes and interprets K–12 assessment data, maintains confidential student and school performance files, develops achievement profiles and reports, and trains educators and administrators to use data for instructional improvement. The role focuses on identifying at-risk students, tracking achievement gaps, validating reported data, supporting school improvement initiatives, and communicating statistical findings to diverse stakeholders.

Develop and optimize machine learning and statistical models for fraud detection and anti-money laundering. Analyze large datasets to identify fraudulent patterns, build scalable data pipelines and features, evaluate model performance, and collaborate with engineering, product, and operations teams. Communicate insights to technical and non-technical stakeholders while maintaining data privacy and security compliance. The role offers significant ownership in building Oscilar’s early machine learning stack within a remote-first startup.

Tune AML and fraud detection models using customers’ live transaction data. Investigate missed cases, alert quality, pattern drift, and false positives; validate changes through backtesting and sample review; document regulatory rationale; and present model performance and business value to compliance, risk, and data science stakeholders. Partner with customer teams on account strategy and relay customer-specific or systemic findings to product and model functions.

Data Scientist Interns will automate processes, scrape and collect large datasets, and conduct advanced econometric analyses to investigate fraud across finance, healthcare, and cryptocurrency domains. The role involves applying data science and econometric techniques, contributing to individual and team projects, and translating academic expertise into practical industry applications.