Applied Scientist, Demandbase

• Experience: 5+ years in data science or applied machine learning roles, with demonstrated experience in building and deploying models
• Education: Degree in Statistics, Computer Science, Machine Learning, or a related field (e.g., Mathematics, Physics)
• ML Expertise: Proven experience applying ML to at least one area:
• Ranking & Recommendation Systems
• Ad Optimization, Real-Time Bidding Algorithms
• Reinforcement Learning
• A/B Testing and Experimentation
• Strong proficiency in Python and ML libraries (e.g., Scikit-Learn, Pandas, Numpy)
• Familiarity with ML frameworks such as TensorFlow, Keras, PyTorch, and SparkML
• Experience with Scala (preferred) to optimize data workflows and pipelines
• Solid SQL and No-SQL skills
• Cloud & Data Tool Familiarity: Experience with Google Cloud Platform or AWS preferred, with BQML or Spark as a plus

Desirable

• Join Demandbase’s Central Data Science team as an Applied Scientist, where you’ll use machine learning to enhance every stage of the B2B buyer journey
• In this role, you will design, implement, and productize data-driven models to deliver insights, optimize ad targeting, and personalize experiences across our platform
• This position combines practical ML/AI application with cross-functional collaboration to ensure models are both impactful and seamlessly integrated into our offerings
• As an Applied Data Scientist, you’ll work closely with product managers, engineers, and designers to build and refine ML models, with a preferred focus on Scala for supporting and optimizing data pipelines
• Model Development and Implementation: Develop and apply machine learning algorithms to optimize key performance indicators across the customer journey
• Data Analysis and Feature Engineering: Extract actionable insights from large datasets and engineer features that enhance model accuracy and relevance
• Cross-functional Collaboration: Partner with data engineering, product management, and UX teams to translate data-driven models into production-ready solutions
• Model Productization: Work alongside engineers to operationalize ML models, ensuring performance consistency and effectiveness in production environments
• Continuous Learning and Improvement: Stay current with ML research and adapt new methodologies to solve complex business problems
• Account Intelligence & Scoring: Use ML to rank and prioritize accounts within the B2B buying journey
• Real-Time Intent Detection: Develop models to capture intent signals that drive real-time sales and marketing actions
• Ad Optimization and Personalization: Build models to predict engagement and optimize ad campaigns using data from tens of billions of daily auctions