Data Scientist

About Triple Lift

We're Triple Lift, an advertising platform on a mission to elevate digital advertising through beautiful creative, quality publishers, actionable data and smart targeting. Through over 1 trillion monthly ad transactions, we help publishers and platforms monetize their businesses. Our technology is where the world's leading brands find audiences across online video, connected television, display and native ads. Brand and enterprise customers choose us because of our innovative solutions, premium formats, and supportive experts dedicated to maximizing their performance.

As part of the Vista Equity Partners portfolio, we are NMSDC certified, qualify for diverse spending goals and are committed to economic inclusion. Find out how Triple Lift raises up the programmatic ecosystem at

Triple Lift, a fast‑growing startup tackling some of the most challenging problems in the world of digital advertising, is seeking a Data Scientist with a focus on optimization strategies for our real‑time ad marketplace. As a Data Scientist at Triple Lift, you will contribute to experiments to understand bidding behaviors in our real‑time marketplace, improving the performance of traffic‑shaping algorithms, and optimizing outcomes for Publishers, DSPs and Triple Lift alike.

You will build proof‑of‑concept models, put ML models in production, create reusable features and data structures, and collaborate with cross‑functional teams to drive Data Science initiatives forward.

• Conduct research and experiments to improve the performance of our traffic‑shaping algorithms and maximize performance for our customers
• Build new traffic‑shaping optimization strategies based on cutting‑edge research, build proof‑of‑concept ML models, and drive them to successful outcomes in full‑scale production
• Support analytics projects in partnership with Product, Engineering, and cross‑functional teams to support and influence product strategies
• Monitor and measure statistical modeling performance, and build dashboards and alerts to ensure models are functioning effectively
• Build reusable modules and data structures, and provide guidance and feedback to team members on their work, taking into account their skills, backgrounds and working styles
• Take ownership of complex technical challenges, demonstrating initiative in navigating ambiguity and championing engineering best practices and innovation

• Bachelor’s degree or higher in a related quantitative field (e.g. Mathematics, Computer Science, Engineering)
• 2+ years of work experience in Data Science / Machine Learning
• Familiarity with tools used in our tech stack is a plus (e.g. Python, Spark, AWS, Docker, Data Bricks, ONNX, MySQL, Snowflake and Airflow)
• Familiarity with ML libraries like scikit‑learn to quickly analyze data and prototype models that can be used in high‑volume distributed systems
• Familiarity with monitoring and measuring statistical modeling performance via dashboards and alerts, using tools like Prometheus, Grafana, Looker, etc., to make sure models are functioning effectively
• Solid software engineering fundamentals, including version control, testing, and writing clean, maintainable, and production‑ready code
• Excellent technical communication skills
• Strong analytical and problem‑solving skills
• Committed to a process of continuous learning and spreading subject matter expertise

From Our Early Days, We’ve Always Believed In Using The Right Tools For The Right Job And Continue To Explore New Technology Options As We Grow. The Data Science Team Uses The Following Technologies At Triple Lift.

• Languages:
  
  Python, Java
• Frameworks:
  Spark, Data Bricks, ONNX, Docker, Airflow
• Databases:
  MySQL, Snowflake, S3/Parquet
• Amazon Web Services to keep everything running

At Triple Lift, we’re a team of great people who like who they work with and want to make everyone around them better. This means being positive, collaborative, and compassionate. We hustle harder than the competition and are continuously innovating. Learn more about Triple Lift and our culture by visiting our Linked In Life page.