YOU MUST HAVE:

• Qualified candidates will possess a Bachelor’s degree (or foreign equivalent) in Data Science, Information Systems, Economics, Math, Finance, Business, Electronic Engineering, or related field and 5 years of industry experience designing, developing and implementing data-driven solutions across functional areas.

• Must have 3 years of experience with the following: experience in data analytics, and delivering actionable business insights; machine learning experience applied to real world processes, systems and infrastructure in a commercial environment; proven experience leading delivery of AI projects; proven experience and highly proficient in data extraction and data manipulation using SQL, visualization and reporting.

• Telecommuting permitted up to 2 times per week.

ADDITIONAL INFORMATION:

• Job Site: Atlanta, GA

• 40 hours/week

• Eligible for Employee Referral Program: $1500

• If offered employment must have legal right to work in U.S. EOE.

Lead Data Scientist for Honeywell International, Inc. (Atlanta, GA)RESPONSIBILITIES:

• Provide insights, data analysis and predictive analytics in support of e Commerce digital and other functional initiatives.

• Design and implement statistical and ML based methods to create models, identify changes in customer behavior and forecast ecommerce results.

• Require a strong foundation in machine learning, predictive analytics and NLP.

• Collaborate with functional teams to deliver AI/ML/LLM solutions that drive business impact.

• Support data analytics and reporting for business functions.

• Present data-based recommendations and “tell the story” that enables effective decision-making.

• Provide thought leadership as it relates to analytics.

• Provide database systems support, end-to-end development support and technology upgrade oversight

• Lead the delivery of machine learning solutions like forecasting, churn prediction, NLP, and ecommerce analytics use cases.

• Lead initiatives using NLP to extract insights from unstructured data for use cases including root-cause analysis.

• Guide dashboard development (Tableau/Power BI) and data storytelling to communicate insights effectively to technical and non-technical stakeholders.

• Mentor junior data analysts and promote best practices in ML development and data analytics.

• Collaborate with cross functional teams to drive strategic outcomes.