Data Scientist - Havis Inc

Job Title: Data Scientist

Department: Finance

Accountability: This Position Reports to the Senior Manager, Financial Planning & Analytics

We are building a next-generation analytics capability at the intersection of data, finance, operations, and strategy. The Data Scientist will support this effort by developing practical, scalable analytics, machine learning, and AI-enabled solutions that improve decision-making, strengthen reporting accuracy, and enhance business performance. This role combines data engineering, database design, statistical modeling, business intelligence, and process improvement to transform complex structured and unstructured data into timely, meaningful insights. Working closely with Finance, Operations, IT, and other business stakeholders, the Data Scientist will gather requirements, define analytical approaches, build and validate models, automate data workflows, and deliver reporting tools that support efficient and effective business operations. This is an excellent opportunity for an early-career professional with approximately 3–5 years of relevant experience who is highly analytical, technically capable, business-minded, and motivated by the opportunity to apply emerging AI technologies to real-world business challenges.

Primary Responsibilities:

• Partner with Finance, Operations, IT, and functional leaders to understand business objectives, document requirements, assess current-state processes, and recommend data-driven solutions that improve efficiency, accuracy, and decision-making.
• Design, develop, test, and maintain analytical models, predictive tools, reporting datasets, and AI-enabled solutions using Microsoft, SQL, Python/R, LLM, and related technologies.
• Build, maintain, and optimize ETL/ELT pipelines that ingest, cleanse, transform, and validate data from multiple internal and external sources, including structured and unstructured datasets.
• Develop dashboards, recurring reports, self-service analytics tools, and executive-level visualizations that translate complex data into clear, actionable business insights.
• Apply machine learning, statistical analysis, forecasting, classification, optimization, and other advanced analytical methods to support planning, performance management, operational improvement, and strategic initiatives.
• Provide technical guidance for data and analytics initiatives, ensuring solutions align with business priorities, data governance expectations, security practices, documentation standards, and scalable architecture principles.
• Support enterprise-level data workflows, system interfaces, third-party software integrations, and modernization efforts, including evaluating legacy system risks and recommending long-term improvements.