Data Scientist- AI Engineer

Redwood Software

About The Position

As the Data Scientist – AI Engineer, you will transform revenue data into AI-powered intelligence that drives smarter, faster, and more predictive decision-making across the business. You will lead the application of advanced analytics, machine learning, and generative AI to uncover patterns in sales performance, customer behavior, and revenue dynamics—turning complex data into clear strategic direction. By building scalable predictive models and embedding AI into revenue workflows, you will help forecast growth, reduce churn, optimize sales strategies, and identify high-value opportunities. Your work will directly influence executive decision-making, accelerate insight generation, and position AI as a core driver of measurable business impact across the revenue organization.

Requirements

Bachelor's degree in Business Analytics, Data Science, Statistics, Computer Science, Economics, or a related quantitative field. Master's degree preferred.
3+ years of experience in a data science role, preferably within a Revenue Operations or Sales Operations function at a B2B software company.
Proven experience with CRM systems (e.g., Salesforce) and marketing automation platforms.
Strong experience with SQL for data extraction, manipulation, and analysis.
Experience with statistical analysis and predictive modeling using Python (pandas, numpy, scikit-learn)
Experience using AI-assisted analytics tools (LLM copilots, automated feature engineering, AutoML, etc.)
Ability to design AI workflows that accelerate decision-making with tools like Claude Code, ChatGPT and Gemini
Exceptional analytical and problem-solving skills with a keen eye for detail.
Ability to work independently and collaboratively in a fast-paced environment.
Strong communication and presentation skills, with the ability to explain complex data concepts to non-technical audiences.
Proactive, self-starter with a strong sense of ownership and accountability.
Understanding of sales processes, SaaS business models, and key revenue metrics.
Comfort integrating generative AI (LLMs) into workflows for insight generation, summarization, or automation

Responsibilities

Conduct in-depth analysis of sales cycles, conversion rates, customer churn, and other revenue-driving activities using advanced AI-powered analytics tools and large-scale data processing techniques to identify trends, opportunities, and areas for improvement.
Leverage generative AI, machine learning platforms, and automated analytics solutions to accelerate insight generation and improve analytical depth and speed.
Translate complex, AI-derived data findings into clear, concise, and compelling narratives for various stakeholders, including executive leadership.
Perform ad-hoc, AI-assisted data analysis to support strategic initiatives and answer critical business questions, utilizing automation to increase responsiveness and analytical scalability.
Apply advanced statistical methods, machine learning, and AI techniques (including supervised, unsupervised, and deep learning approaches where appropriate) to forecast revenue, predict customer behavior (e.g., churn risk, upsell potential), and optimize sales strategies.
Design, develop, and deploy AI-driven predictive models to segment customers, identify ideal customer profiles (ICPs), enhance lead scoring, and automate decision-making processes.
Utilize AI development frameworks and MLOps best practices to build scalable, production-ready models and continuously improve model performance.
Collaborate with stakeholders to define business problems and translate them into AI-powered data science solutions, proactively identifying opportunities where AI can create measurable business impact.
Continuously evaluate, monitor, and refine models for accuracy, fairness, and impact, leveraging automated retraining, experimentation platforms, and model performance tracking.
Champion the responsible and effective use of AI across revenue analytics, ensuring transparency, governance, and alignment with business objectives.