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    Data Scientist - Dallas, United States - Lennar Homes

    Lennar Homes
    Lennar Homes Dallas, United States

    1 week ago

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    Description

    Overview:

    The primary mission of the Lead Data Scientist role is to help our business evolve into an insights-driven organization. The position sits in our Emerging Technologies and Advanced Analytics Solutions team, which aims to disrupt our industry with emerging technologies and data science solutions that drive sustainable competitive advantage for Lennar. The Lead Data Scientist will develop machine learning models to provide insights and recommendations to the business to improve internal operations, to understand customers, to provide smarter products and services, to create additional revenue, and to drive decision making. This role will help lay the enterprise-ready foundation for data science across Lennar.

    Responsibilities:
    • Formulate Data Science Problems: Collaborate with cross-functional teams, including land management, construction, procurement, marketing, sales, and finance, to identify business problems and opportunities where data science and machine learning, including generative AI, can add value.
    • Lead data science projects: Design and execute end-to-end data science projects, from problem definition and data collection/preparation to modeling, interpretation of results, and development of actionable insights and recommendations.
    • Statistical modeling and machine learning: Utilize statistical techniques, machine learning algorithms, and generative AI approaches to develop algorithms and uncover patterns, develop predictive models, and create data-driven solutions for various business challenges.
    • AutoML/Data Science Tooling: Be comfortable exploring and using advances in AutoML and other augmentative technologies to expedite model development and validation.
    • Establish a strong machine learning foundation: Work closely with our machine learning operations team to build and maintain a robust machine learning infrastructure, frameworks, and pipelines, ensuring scalability, reproducibility, and efficiency in model development and deployment.
    • Experimentation: Generate hypotheses, design and execute experiments, validate the hypotheses, and effectively communicate findings and recommendations.
    • Data management and governance: Ensure the consistency, accuracy, and quality of data used in modeling and analysis, working closely with data engineers and data architects to implement data governance best practices.
    • Communicate Insights: Extract meaningful insights from large datasets and communicate findings to both technical and non-technical stakeholders through visualizations, presentations, and reports.
    • Mentor junior data scientists: Oversee a team of data scientists, providing guidance, mentorship, and expertise in statistical and analytical techniques, as well as generative AI methodologies.
    • Stay up-to-date with advancements in data science and generative AI: Continuously enhance your knowledge of the latest trends, tools, and techniques in data science, machine learning, generative AI, and predictive analytics, and apply them to improve our data-driven capabilities.
    • Collaborate with external partners: Engage with external partners, including vendors, research institutions , and industry experts, to explore opportunities for innovation, access to additional data sources, and collaboration on cutting-edge projects related to generative AI and machine learning.
    Qualifications:
    • Education: A bachelor's degree in Computer Science, Mathematics, Statistics, or a related quantitative field is required. A master's or PhD in Data Science, Machine Learning, or a related field is strongly preferred.
    • Work experience: At least 7 years of experience working as a Data Scientist/Analyst on data science applications, with a proven track record of leading successful data science projects. Prior experience in implementing generative AI and building a strong machine learning foundation is highly desirable. Prior experience in the home building or real estate industry is a plus.
    • Strong analytical skills: Demonstrated expertise in data analysis, statistical modeling, machine learning techniques, and generative AI approaches. Experience with tools and programming languages such as Python, R, SQL, and data visualization tools is essential.
    • Project Leadership abilities: Proven experience in leading data science projects end-to-end, mentoring junior data scientists along the way. Strong communication and interpersonal skills are required to effectively collaborate with cross-functional teams and senior stakeholders.
    • Business acumen: Understanding of the home building industry and the ability to translate business problems into data-driven solutions. Familiarity with asset risk management, scheduling optimization, pricing optimization, financial forecasting, customer segmentation, demand forecasting, and generative AI applications is highly desirable.
    • Problem-solving mindset: Ability to think critically, identify patterns, and creatively apply analytical techniques, including generative AI, to solve complex business challenges.
    • Data governance and ethics: Familiarity with data management principles and best practices, including data quality, data privacy, and ethical considerations of working with sensitive data.
    • Flexibility and adaptability: Willingness to adapt to a fast-paced and rapidly changing environment. Demonstrated ability to manage multiple projects simultaneously, prioritize tasks, and meet deadlines.
    • Technologies and Methodologies:
    • Proficiency in building data pipelines to feed ML/NLP models
    • Expertise in machine learning frameworks such as scikit-learn, TensorFlow, Kera, H20.a
    • Strong adherence to core software engineering principles (code modularization, versioning, git, testing etc.)
    • Proficiency in machine learning frameworks such as scikit-learn, TensorFlow, Kera,
    • Experience building generative AI applications in a business setting
    • Proficiency in SQL and RDBMS
    • Proficiency in cloud technologies and their application to data science.
    • Experience working with NoSQL database systems and big data technologies
    • Experience working with knowledge graphs and building data science models on them is a significant plus
    Type:
    Regular Full-Time
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