Perform exploratory data analysis (EDA) and data wrangling on raw, messy datasets to extract actionable business insights.
Design, build, train, and evaluate end-to-end machine learning models including regression, classification, clustering, and ensemble-based approaches.
Develop and implement predictive analytics solutions using traditional machine learning techniques such as Linear and Logistic Regression, Random Forests, and XGBoost.
Integrate modern AI capabilities such as NLP, generative AI, or advanced predictive analytics into existing products and client solutions.
Handle end-to-end ML workflows, from data preparation and feature engineering to model validation and deployment support.
Perform basic data engineering and data cleaning tasks independently in environments with evolving infrastructure.
Collaborate closely with clients and internal stakeholders to understand requirements and translate them into technical solutions.
Professionally challenge and defend technical decisions using data, metrics, and analytical insights when dealing with demanding stakeholders.
Adapt quickly to changing priorities, working across multiple initiatives such as model development, SQL-based analytics, and urgent dashboards.
Contribute to AI solution design in startup-like or consulting environments where ambiguity and rapid iteration are expected.
Bachelor’s degree in Computer Science, Software Engineering, or a related field.
A minimum of 5+ years of professional experience in data science, machine learning, or AI engineering roles.
Strong background in data science foundations including exploratory data analysis, data wrangling, and deriving business insights from complex datasets using Python, Pandas, and SQL.
Proven experience building, training, and evaluating traditional machine learning models from scratch, including Linear Regression, Logistic Regression, Random Forests, XGBoost, classification, and clustering techniques.
Demonstrated ability to bridge traditional data science with modern AI applications such as NLP, predictive analytics, or generative AI.
Hands-on experience delivering end-to-end machine learning solutions in production or client-facing environments.
Comfort working on the “dirty work” of data, including cleaning, preprocessing, and basic data engineering in the absence of mature infrastructure.
Experience working in consulting, freelance, or startup environments with evolving roadmaps and limited operational support.
Strong stakeholder management skills, with the ability to use data and insights to push back professionally and defend technical decisions.
High adaptability and comfort with frequent context-switching across multiple technical domains.
Ability to operate effectively without a fully mature data warehouse or dedicated MLOps team.
Excellent English communication skills.
