Top Tech Jobs & Startup Jobs

Leads end-to-end development and production deployment of machine learning, generative AI, and agentic solutions for payments and banking. Responsibilities include problem framing, experimentation, evaluation, MLOps, LLMOps, governance, monitoring, scalable cloud architecture, security, and systems integration. The role establishes reusable AI capabilities, partners with cross-functional stakeholders, mentors technical teams, and communicates model outcomes and operational tradeoffs to technical and non-technical audiences.

Applies advanced statistical and machine learning methods to B2B supply optimization, builds datasets and models, designs experiments, creates visualizations and dashboards, and translates analysis into business recommendations. Leads analytical workstreams, automates reporting, documents reproducible code, communicates with senior stakeholders, and mentors junior analysts.

Develops data science and analytics solutions for Global Payments by exploring transaction flows, identifying high-impact use cases, delivering actionable insights, and supporting data-driven product strategy. Collaborates with stakeholders and product managers, translates complex data requirements into analytical solutions, promotes data products, and contributes to robust data assets and product enhancements.

Partner with Marketing to own attribution, ROI, conversion, and funnel analysis. Design experiments, diagnose anomalies, optimize spend, and translate data into clear recommendations. Collaborate with Data and Analytics Engineers to shape data models and pipelines, and communicate insights to non-technical stakeholders.

Lead development and deployment of ML systems (neural nets, graph models, Transformers) for AML detection. Mentor team members, integrate models with production platforms and LLM agents, design data strategies, run large-scale training and evaluation, and communicate results to stakeholders.

Design and build end-to-end ML solutions for 1:1 personalization, own data transformation, model training and deployment, provide technical customer guidance, extend product capabilities, and shape BrazeAI strategy.

Turn data into product and business decisions through metric definition, experimentation, causal analysis, deep-dive research, and clear recommendations. Model reliable analytical data in dbt, build scalable dashboards and reporting, partner with data engineering, and influence leaders across Product, Finance, Growth, GTM, and Customer Success. The role also helps establish data-team standards, tooling, and operating practices in an AI-first environment.

Develop and research machine learning solutions for retail insights and product assortment. Perform exploratory analysis, model development and validation, implement efficient Python/Spark code, apply NLP and generative AI approaches, and turn research into reusable science modules while collaborating with engineering, product and client teams.

Analyze Spotify’s planning and execution data to develop hypotheses, statistical and machine learning models, data pipelines, dashboards, and actionable recommendations. The role translates complex operational and strategic information into narratives, reports, and decision frameworks for leaders. It also explores AI and automation for faster insight generation and collaborates with data scientists, engineers, product managers, and business partners across the company.

Evaluate AI model outputs on data science, statistics, machine learning, experimentation, and quantitative problems. Develop expert prompts, datasets, and reference solutions; identify statistical and methodological errors; and provide structured feedback to improve model performance. The role requires Python, machine learning, statistics, data analysis, experimentation, Pandas, and data modeling skills.

Own data strategy and experimentation for a critical product domain, influencing senior leadership decisions through causal inference, forecasting, LTV modeling, retention analysis, pricing analysis, and scenario planning. Define key metrics, lead strategic analytical initiatives, improve measurement frameworks, and mentor senior data scientists while partnering with Product, Engineering, and Business leaders.

Develop statistical, predictive, and machine learning models using large smart-meter datasets to improve water consumption, leakage, demand, and regulatory reporting. Prepare and automate high-frequency data workflows, build reusable quality checks and pipelines, and collaborate with engineers and business stakeholders. Translate analytical findings into visualizations and recommendations while maintaining data ethics, privacy, governance, and regulatory compliance.

Own AI safety datasets from collection through labeling, quality control, versioning, and training readiness. Translate safety policies into consistent labeling and evaluation criteria, manage external contributors, and build production model evaluation workflows. Develop lightweight Python and SQL pipelines for reproducible data analysis, partnering with safety researchers, policy specialists, and ML engineers. The role also contributes to scalable dataset methodologies, quality standards, and human-in-the-loop processes.

Develop, optimize, deploy, and monitor statistical and machine learning models for commercial lending products. Analyze alternative data sources, experiment with model optimization frameworks, identify risks, and communicate technical findings as commercial insights. Collaborate with data engineers, analysts, and business stakeholders while using Python, SQL, and AI techniques to solve complex business problems and improve borrower products.

Build and maintain predictive models for pre-match and in-play sports betting markets. Clean, visualize, analyze, and interpret data; optimize existing models; research new methods; implement models and libraries in Java; support the codebase; and collaborate with product owners, developers, traders, and quantitative analysts.

Build and deploy production machine learning models and fraud decisioning tools for e-commerce transactions. Partner with business teams to improve decisioning performance, automate manual processes, run experiments, and balance model complexity with performance. Write and review Python and SQL code, work with distributed data pipelines using Spark, Databricks, and GCP, communicate findings to technical and business audiences, and participate in an on-call weekend rotation.

Build and deploy production machine learning models and fraud decisioning tools. Partner with business teams to identify performance gaps, automate repetitive work, run experiments, and optimize transaction-risk strategies. Write production and analytical Python code, use SQL and distributed data platforms such as Spark, Databricks, and GCP, and communicate findings to technical and business audiences. Participate in code reviews, peer collaboration, and an on-call weekend rotation approximately six times annually.

Build, evaluate, optimize, and maintain predictive models for pre-match and in-play sports betting markets. Responsibilities include data cleaning, visualization, analysis, model research, Java implementation, testing, library maintenance, and on-demand mathematical analysis. The role collaborates with quantitative analysts, product owners, developers, and traders.

Analyze large datasets, apply statistical and machine learning models, perform data analytics, and create visualizations. Collaborate with founders and enterprise clients to solve complex business problems, embedding developed models into the Kepler AI platform. This early-stage role requires hands-on development, strong analytical communication, and comfort working in an ambiguous startup environment.

Leverages AI, machine learning, predictive analytics, optimization, and data visualization to support strategic business decisions. Identifies quality data sources, develops and validates predictive models, establishes data strategies and governance, deploys scalable production models, monitors performance, and communicates insights to cross-functional stakeholders. The role also supports continuous model improvement and mentors junior team members.

Develop and improve data science and machine learning models for marketing and consumer insights, including attribution, personalization, forecasting, experimentation, and marketing measurement. Prepare datasets, define evaluation metrics, support model deployment, and generate insights from media, pricing, promotions, and syndicated data. Collaborate with product owners, data engineers, and machine learning engineers; contribute to responsible AI governance, code reviews, reusable AI assets, and mentorship of junior team members.

Lead design, build, and deploy enterprise-scale AI solutions and multi-agent systems. Drive model lifecycle, monitoring, retraining, evaluation frameworks, semantic knowledge systems, and integration into operational workflows while communicating results to senior leadership.

Conduct genomic and multi-omics analyses for rare and complex disease projects, including drug target identification, biomarker and diagnostic discovery, and patient stratification. Prepare, quality-control, annotate, harmonize, and analyze whole genome sequencing data. Develop and benchmark computational tools and Nextflow pipelines, support internal and external collaborators, research emerging methods, and communicate findings through reports, publications, and presentations.

Develop and validate agentic AI capabilities for patent analysis using machine learning, NLP, LLMs, RAG, hybrid search, and agentic workflows. Analyze large-scale intellectual property datasets, design experiments, evaluate and benchmark models, define reasoning strategies, and translate findings into product recommendations. Collaborate with product managers, IP experts, and engineers to deliver scalable, customer-facing solutions.

Own experimentation and analytics for iwoca’s partner lending channel. Design experiments, apply causal inference, model customer behavior, analyze large datasets, optimize conversion funnels, and translate findings into product and commercial decisions. The role involves shaping analytical practices, evaluating lending product strategies, using AI to accelerate analysis, and collaborating with commercial, strategy, product, engineering, and design teams.