Collaborate effectively with cross-functional stakeholders to identify questions and business challenges, and develop plans of action to define, design, and develop machine learning models and algorithms.
Apply strong mathematical and statistical principles to generate and test hypotheses, analyze and interpret results, and provide insights based on data analysis.
Navigate large, complex datasets for data mining, profiling, and curation, as well as utilize natural language processing (NLP) techniques.
Design, develop, and program methods, processes, and software programs to consolidate, cleanse, and analyze unstructured, diverse data sources.
Develop and evaluate predictive models and algorithms that optimize value extraction from data to support business process improvements and solve challenges.
Apply generative AI techniques through APIs or custom workflows to enhance data analysis and model development.
Requirements & Skills:
Bachelor’s degree or equivalent in a relevant field, with a minimum of 5+ years of relevant experience.
Strong mathematical and statistical background, with expertise in applying these principles to machine learning and AI model development.
Proficiency in programming languages such as Python, R, or similar, with experience in building AI models and utilizing relevant libraries and frameworks.
Demonstrated skills in exploratory data analysis techniques, machine learning algorithms, model validation techniques, and data visualization techniques.
In-depth knowledge of technical areas such as Snowflake, Python, Spark, R, Shiny, Jupyter, and associated packages and libraries (e.g., numpy, pandas, SciPy, OpenAI).
Familiarity with cloud architectures, including Databricks, lambda functions, Mlflow, SageMaker, TensorFlow, etc.
Proficiency in data engineering, pipelining and wrangling tools, data visualization and modeling tools, and mathematical approaches to imperfect data.
Solid understanding of data management approaches, including relational databases, data schemas, object stores, column stores, triple stores, graph stores, and/or document stores.
Proven ability to deliver accurate work products in a cross-functional matrix environment, managing multiple competing priorities.
Strong analytical skills and ability to develop detailed analysis, models, plan calculations, and tools.
Excellent communication skills, with the ability to effectively communicate and influence stakeholders at all levels.
Demonstrated creativity in identifying non-traditional data sources and applying leading analytic techniques.
Experience working in the software development lifecycle using data science frameworks like Databricks and MLflow is preferred.
