Data Scientist, Demandbase

• Experience: 2–3 years of experience in data science or analytics, with hands-on experience in data processing, exploratory analysis, and some exposure to machine learning
• Education: Degree in Data Science, Statistics, Computer Science, Mathematics, or a related field
• Technical Skills:
• Proficiency in Python and data analysis libraries (Pandas, Numpy)
• Strong SQL skills for data extraction and manipulation
• Experience with machine learning libraries (e.g., Scikit-Learn) and model evaluation techniques
• Data visualization skills (e.g., Tableau, Power BI, or Python libraries such as Matplotlib and Seaborn)
• Familiarity with Machine Learning Concepts: Experience in building and validating models, with an interest in developing more advanced skills

Desirable

• Demandbase is seeking a Data Scientist to join our Central Data Science team, where you’ll contribute to impactful machine learning projects that shape the B2B buyer journey
• This role is ideal for someone with solid experience in data analysis and foundational machine learning, ready to apply their skills in a high-growth environment
• You will work on a range of projects, from account ranking to ad optimization, developing data-driven models that deliver actionable insights and improve business performance
• In this role, you’ll collaborate with experienced data scientists, product managers, and engineers, offering opportunities to develop your machine learning skills and gain experience deploying models in production
• A background in data science, with analytical proficiency and interest in practical ML applications, is ideal
• Data Exploration and Preparation: Analyze and preprocess large datasets to support machine learning models, identifying key insights to guide data-driven decision-making
• Feature Engineering and Model Building: Develop and refine features, assist with model selection, and build foundational machine learning models to optimize business outcomes
• Model Performance and Validation: Evaluate and validate model performance, using metrics to improve accuracy and reliability in production environments
• Cross-functional Collaboration: Work with data engineering, product, and UX teams to integrate models into workflows, ensuring solutions align with business needs
• Communication of Findings: Present analysis and model results to technical and non-technical stakeholders, using data visualizations and clear explanations to convey insights effectively
• Account Prioritization: Support projects to rank accounts based on their journey stage, helping drive targeted sales and marketing efforts
• Intent Signal Analysis: Contribute to identifying intent signals from behavioral data to improve targeting and personalization
• Campaign Optimization: Assist in optimizing ad campaigns by analyzing engagement metrics and supporting model improvements for ad targeting and click-through rate predictions