• Design, develop, and deploy advanced AI/ML models and algorithms that improve operational efficiency, forecasting accuracy, and decision intelligence across the Hunter Douglas supply chain. • Conduct data exploration, feature engineering, and model tuning to ensure accuracy, robustness, and business applicability. • Collaborate with business stakeholders to translate complex challenges into data-driven solutions aligned with organizational goals. • Monitor, retrain, and optimize models post-deployment to maintain high performance as data and business conditions evolve. • Apply state-of-the-art methods such as neural networks, reinforcement learning, natural language processing (NLP), and generative AI to tackle diverse business challenges. • Contribute to the AI strategy roadmap, ensuring alignment with Hunter Douglas’s digital transformation initiatives. • Work with data engineers and IT to ensure efficient data pipelines, scalable infrastructure, and model deployment. • Partner with internal and external technical teams, vendors, and consultants to implement and scale AI solutions. • Communicate findings and insights clearly to both technical and non-technical stakeholders through visualizations, presentations, and storytelling. • Stay at the forefront of AI and analytics innovation, evaluating new tools, methodologies, and technologies for potential adoption. • Collaborate with digital product and IT teams to develop and integrate AI-assisted tools and automations within Hunter Douglas’s VIBE platform and data ecosystem. • Experiment with AI coding assistants (e.g., Cursor, GitHub Copilot, OpenAI Codex) to accelerate analytics and model development and improve reproducibility. • Support the creation of custom AI copilots and prompt libraries for use in digital supply chain tools, analytics dashboards, and planning applications. • Partner with the Digital Supply Chain Leader to prototype intelligent process automation for recurring planning, logistics, quality, or operational workflows.

• Provide thought leadership in data science techniques • Analyze and manage business objectives in complex systems development • Collaborate and build relationships with clients • Research and implement best practices for enterprise processes • Support executive decision making through visualization • Model complex systems and lead digital transformation • Facilitate understanding and organizational learning

• Lead and implement advanced remote sensing analysis tasks, including processing of optical and SAR data, timeseries analysis, and predictive modeling. • Design and implement machine learning pipelines for geospatial analysis, including feature engineering, model selection, hyper parameter tuning, and validation. • Develop and deploy deep learning models (CNNs, RNNs, LSTMs, Transformers) for image classification, segmentation, object detection, and time series forecasting. • Apply advanced AI techniques for predictive modelling and mapping of indicators relevant to ecosystem health assessment. • Process and analyze optical data (Sentinel 2, Landsat 8/9) and SAR data (Sentinel 1). • Implement time series analysis and forecasting models. • Develop scalable, reproducible spatial data processing workflows and contribute to MLOps practices. • Supervise a team of junior spatial data scientists and developers. • Lead internal capacity development seminars on machine learning and AI applications.

This description is a summary of our understanding of the job description. Click on 'Apply' button to find out more. Role Description As the Data Science Manager, you’ll have a big mission. Responsible for assuring the Company’s standards are achieved and excellent customer service is delivered. You also understand the value of taking a moment to express gratitude to the village that helped to make it happen. As you can likely tell, 'how' things are done matters just as much as ‘what’ was done here at BH! Key Responsibilities - Design, build, and deploy statistical models, forecasting frameworks, and predictive analytics solutions. - Apply advanced analytical techniques—including regression, time‑series forecasting, classification, clustering, and experimentation—to address strategic business challenges. - Lead model validation, performance monitoring, and documentation to ensure accuracy, rigor, and reproducibility. - Identify high‑impact machine learning and AI use cases and guide their implementation. - Write advanced SQL queries and manage analytics workflows within Snowflake and related cloud data environments. - Use Python and R extensively for modeling, experimentation, and data analysis. - Lead, mentor, and develop a team of analysts, fostering both technical and professional growth. - Establish and uphold technical standards, analytical methodologies, and best practices across the team. - Review analyses, models, and dashboards for quality, accuracy, and business relevance. - Prioritize initiatives and allocate resources in alignment with executive priorities. - Expand team capabilities in data science, statistical modeling, and AI applications. - Partner directly with executive leadership to clarify analytical needs and shape complex business questions. - Translate analytical findings into clear, concise, executive‑ready insights and recommendations. - Serve as a strategic advisor on forecasting, performance improvement, and risk‑management initiatives. - Oversee the development and maintenance of executive‑level Power BI dashboards and semantic data models. - Ensure data integrity, consistency, and accuracy across all reporting and analytical outputs. - Collaborate with cross‑functional teams in Hex or similar notebook platforms to document, automate, and operationalize analytical work. - Leverage AI tools such as ChatGPT and Claude to accelerate analysis, automate repeatable workflows, and enrich insight generation. - Establish best practices for responsible and effective AI usage within analytics workflows. - Train leaders and business partners on the practical application of AI and analytical tools. - Stay current on emerging data science and AI technologies and implement solutions that deliver measurable business value. - Other duties as assigned. Qualifications - 7+ years of experience in data science, advanced analytics, or a related quantitative field, preferably in multifamily, real estate investment, or financial services. - 2+ years of experience leading or mentoring analysts. - Advanced proficiency in SQL, Python, and R. - Experience working with Snowflake or similar cloud data warehouse platforms. - Strong experience developing and validating statistical and predictive models. - Advanced experience building executive-facing dashboards in Power BI. - Demonstrated ability to translate complex modeling outputs into clear, actionable business recommendations. - Strong financial and business acumen, particularly in operational or asset-based environments. - Proven ability to independently scope ambiguous problems and deliver high-quality analytical solutions. Requirements - Seniority Level: Experienced - Industry: Property Management - Employment Type: Full-Time - Location: Remote - Work Schedule: 8am-5pm, Monday-Friday, or as needed to meet business needs. Benefits - BH is an Equal Employment Opportunity Employer. - We foster the diverse voices of our community by advocating for inclusivity, celebrating our differences, and continually evolving our practice to make BH a better place to work and live. - Our posted compensation reflects the cost of talent across multiple US geographic markets. - Pay is based on a number of factors and may vary depending on job-related knowledge, skills, and experience.