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Mid-level ML/data role at a well-known brand in a metro location increases applicant competition.
ML/NLP skills transferable across industries, but support-operations domain experience moderately increases specificity.
Explicit 3–5 years requirement plus mandatory ML/NLP/LLM and tooling skills enforces strict filters.
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Develop and implement methods to analyze large qualitative and quantitative datasets to generate actionable insights and identify improvement opportunities.
Build and maintain predictive and classification machine learning models, including NLP and Generative AI solutions, to solve business problems and improve support workflows.
Create automated data pipelines, interactive dashboards, and communicate complex analytical findings to technical and non-technical stakeholders, including leadership.
Bachelor's degree in Data Science, AI, Statistics, Mathematics, Computer Science, Engineering, or related quantitative field.
3–5 years of professional experience in data science, machine learning, or advanced analytics roles.
Strong proficiency in Python (Pandas, NumPy, Scikit-learn, Matplotlib/Seaborn) and SQL for data handling and analysis.
Experience with statistical analysis, machine learning models (classification, regression, clustering, NLP), and data visualization tools such as Tableau or Preset.
Experience applying modern NLP techniques and libraries (e.g., spaCy, Hugging Face Transformers, NLTK) and Generative AI/LLM based solutions to real-world business problems.
Proven ability to collaborate cross-functionally to translate business requirements into technical solutions and communicate complex analytics clearly.
Hands-on experience with building automated data pipelines and using AI-assisted development tools to optimize code and accelerate development.