The Data Scientist I supports statistical analyses of large datasets, the development and deployment of Machine Learning (ML), multimodal Deep Learning (DL), and Artificial Intelligence (AI) solutions that improve the relevance and value of ads across our Ad Exchange, Contextual Platform, and Attention Measurement Platform. This role focuses on applying strong analytical foundations, building and evaluating ML and DL models, leveraging general AI concepts and techniques, while also partnering closely with the Engineering, Product, and Data Science team to improve ad-serving performance, operational decision-making and lead development of new AI products that best serve the business. 

The ideal candidate is curious, data-driven, and eager to develop a strong understanding of the advertising domain and the systems that power large-scale decision-making. You will work with large datasets, contribute to production AI systems, and gain hands-on experience across the ML lifecycle — from exploration to monitoring.

Note: GumGum fosters a flexible work environment, offering GumGummers the ability to work either in-office or remotely/from home. For this position, in-person/office collaboration is required 2 days per week, supporting a balanced approach to flexibility and team engagement.

What You'll Achieve

• Support the translation of business and product requirements into data-driven analyses and ML solutions
• Partner with Engineering team members and senior Data Scientists to develop, test, and deploy ML and DL models
• Conduct exploratory data analysis to inform feature development and modeling approaches
• Build, run, and maintain regular pipelines to analyze production data, generate KPIs, and prepare automatic retraining of existing models
• Query, clean, and structure large datasets using SQL, Spark, and cloud data platforms
• Train, evaluate, and iterate on traditional ML models and multimodal deep learning models under guidance from senior team members
• Design and maintain Looker dashboards and other Business Intelligence (BI) tools to track Key Performance Indicators (KPI) for key stakeholders
• Develop and deploy agentic pipelines and other LLM-powered applications, including prompt engineering, tool use, and evaluation of model outputs
• Contribute to existing Machine Learning Engineering (MLE) workflows for model training, deployment, and monitoring
• Document analyses, models, and broader learning to support knowledge sharing across the team and non-technical audiences
• Continuously expand on statistical and AI foundations while learning new AI/ML techniques, tools, and advertising-domain concepts

Skills You'll Bring

• Bachelor’s degree in a quantitative field (e.g., Statistics, CS, Math, Physics, or Economics).
• 1–2+ years in a data-driven role such as Analytics, Data Science, or ML Engineering.
• Proficiency in Python and experience applying ML/DL methods using libraries like scikit-learn, PyTorch, HuggingFace, or OpenCV.
• Dependable SQL skills and experience designing pipelines or DAGs using tools like Airflow or Astronomer.
• Exposure to cloud environments (AWS/GCP, Databricks, Snowflake) and large-scale query tools like Spark or Snowpark.
• Strong grasp of A/B testing, experimental design, and statistical concepts (regression, classification, optimization).
• Experience with LLM prompting and interest in frameworks like RAG, LangChain, or agentic systems.
• Ability to design dashboards for diverse audiences and collaborate effectively with Product and Engineering teams.

What We Offer

At GumGum, competitive base pay is a part of a total rewards package, which also includes benefits, an emphasis on recognition, development, and wellness. The reasonable estimated base pay range for this role is from $128,000-$130,000 annually. The actual amount may be higher or lower. Individual compensation will vary based on factors including, but not limited to, relevant qualifications, work location, and labor market conditions.

The total rewards package offered also includes an employer-matched 401(k) retirement plan, and, depending on the role, participation in a bonus, commission, or stock incentive program. Your recruiter can share more specifics during the hiring process. Learn more about our U.S. benefits & perks package at gumgum.com/benefits.

Local or 100% Remote

About Point

✨ Real Impact, Real People: Our mission at Point is to make homeownership more valuable and accessible. Your work directly helps homeowners access their wealth, achieve financial flexibility, and realize life changing goals.

✨ Funding: With over $175M raised from top investors like Andreessen Horowitz, WestCap, Greylock, and Prudential, we’re scaling fast! You have the opportunity to join us at a pivotal stage.

✨ Game-changing Product: We're building a category defining company in home equity. We’ve earned a 4.7 Trustpilot rating and an A+ from the BBB, a testament to the value we provide to our 20,000+ customers.

✨ Great Place to Work: Our employees love working here! We are a Certified Great Place to Work and a Fortune Best Workplaces in the Bay Area.

✨ Remote First Culture, Genuine Connection: Work from anywhere in the U.S., while staying closely connected through virtual collaboration, team gatherings, and a people-first culture.

About the role

Point is looking for a Senior Data Engineer to join a lean, high-impact data team that powers analytics for 15+ business functions across a home equity investment company. You'll own the development and maintenance of a 1,600+ model dbt platform, build and optimize ETL pipelines in Airflow, manage Snowflake infrastructure (DDL deployments, permissioning, cost optimization), and ensure the reliability of 40+ data source integrations. This is a hands-on engineering role where your work directly shapes how the business makes decisions, and you'll also have the opportunity to contribute to emerging AI initiatives as the team expands into Cortex AI and LLM-powered workflows.

Your responsibilities

• Develop, maintain, and optimize dbt models across 20+ data marts serving business functions including marketing, production, servicing, finance, compliance, investor operations, and data science
• Build and maintain ETL/ELT pipelines using Airflow (Astronomer), including custom operators, hooks, and sensors for data ingestion from 40+ sources
• Manage Snowflake DDL deployments via schemachange — databases, schemas, warehouses, users, roles, network policies, integrations, and UDFs
• Maintain and extend Terraform-managed RBAC grants (200+ .tf files), ensuring secure, auditable access provisioning across the Snowflake environment
• Drive Snowflake cost optimization — warehouse sizing, multi-clustering configuration, query performance tuning, and monitoring to target 15–25% cost reduction
• Support CI/CD pipelines for dbt Cloud, Astronomer deployments, and automated data quality testing
• Develop and maintain AWS Lambda functions supporting data integrations, APIs, and serverless data processing workflows
• Perform code reviews, enforce engineering standards, and contribute to data model documentation (YAML configs, markdown docs) across the platform
• Collaborate with analysts and data scientists to build and maintain data marts, feature engineering tables, and semantic layer metrics that enable self-service analytics
• Contribute to the team's AI/ML initiatives — including Cortex AI semantic models, data preparation for LLM workflows, and infrastructure supporting data science deployment pipelines 

About you

• 5+ years of experience in data engineering or analytics engineering, with significant hands-on dbt and SQL development experience
• Deep Snowflake expertise — DDL management, role-based access control, warehouse configuration, performance tuning, and cost optimization
• Strong experience with Apache Airflow (or similar orchestration tools) — building DAGs, custom operators, scheduling, and failure handling
• Proficiency in Python for data pipeline development, scripting, Lambda functions, and CI/CD automation
• Experience with Infrastructure as Code — Terraform for access management, schemachange or Flyway for database migrations, version-controlled deployments
• Solid understanding of data modeling — dimensional modeling, fact/dimension tables, data mart design, and enterprise data architecture
• AWS experience — Lambda, S3, SAM/CloudFormation, and serverless architecture patterns
• Strong Git workflows — branching strategies, pull request reviews, CI/CD integration (GitHub Actions or similar)
• Excellent troubleshooting skills — ability to diagnose pipeline failures, data quality issues, and performance bottlenecks across a complex platform
• Clear communicator — can work with non-technical stakeholders to translate business requirements into data models and explain technical tradeoffs

Our benefits 

• Generous health benefits: We provide comprehensive medical, dental, and vision plans with options for flexible spending accounts (FSA) and health savings accounts (HSA).
• Unlimited paid time off: Recharge with unlimited paid time off and 10 company holidays. 
• Flexible remote and onsite work: Our teams work from many different locations and time zones. We support fully remote work and also have an amazing in-person environment in our downtown Palo Alto, CA HQ. 
• Fully paid parental leave: Point will supplement state Paid Family Leave (PFL) so employees receive 100% of their regular base pay, plus two additional weeks of fully paid leave after state PFL ends. In states without PFL, Point offers up to 8 weeks of paid parental leave. In addition, employees also receive 4 weeks of fully paid transition time, during which you may work 2–3 days per week while receiving full base pay.
• Equity: We offer meaningful equity because we believe in sharing the value you help create. Your contributions directly impact our growth, and your equity gives you a stake in our future success.
• Financial wellness:  We provide 401K retirement plans for employees as well as guaranteed life insurance and short- and long-term disability coverage.
• Extra work/life benefits: We provide monthly stipends for internet, mobile plans, wellness perks, a one-time home office reimbursement, and company provided equipment including a MacBook and monitor.

Compensation at Point will be determined by skills, experience, and geographic location. Point has identified the expected annual base salary for this role at this level based on the market by tiers (Region | Location | Market Salary):

• Tier 1 | San Francisco Bay Area, New York, and Seattle | $177,650 - $196,350 
• Tier 2 | Chicago, Austin, Denver, Boston, Washington DC, San Diego, Portland, Sacramento, Philadelphia, Los Angeles & Santa Barbara | $161,500 - $178,500
• Tier 3 | All other US metro areas | $154,850 - $171,150

This does not include any other potential components of the compensation package, including equity, benefits, and perks outlined above. At the launch of each position, we benchmark compensation to the appropriate role and level utilizing competitive compensation data from various data sources as references. At the offer stage, we use the signal we received from our interviews, coupled with your experience, location, and other job-related factors, to determine final compensation.

Location Requirement: This is a remote position. However, candidates must reside in one of Point’s states of operation: AL, AZ, AR, CA, CO, CT, DC, FL, GA, IL, KS, KY, MA, MD, MI, MN, MO, NH, NV, NJ, NY, NC, OH, OR, PA, SC, TN, TX, UT, VA, WA, WI.

Point is proud to be an equal-opportunity employer. We provide employment opportunities regardless of age, race, color, ancestry, national origin, religion, disability, sex, gender identity or expression, sexual orientation, veteran status, or any other protected class. Each individual at Point brings their own perspectives, work experiences, lifestyles, and cultures with them, and we believe that a more diverse team creates more innovative products, provides better services to customers, and helps us all grow and learn.