Data Scientist – Data Analytics & AI (W2)

We are seeking an experienced Data Scientist with strong expertise in data analysis, machine learning, artificial intelligence, and statistical modeling. The ideal candidate will be responsible for analyzing complex datasets, developing predictive models, applying AI techniques, and translating data-driven insights into actionable business solutions.

The candidate should be comfortable working across the full data science lifecycle, from data collection and exploration to model development, evaluation, deployment, and monitoring.

Key Responsibilities
Analyze large and complex datasets to identify trends, patterns, correlations, and business opportunities.
Perform exploratory data analysis (EDA), data profiling, data cleansing, and statistical analysis.
Develop and implement machine learning and AI models for prediction, classification, forecasting, and optimization.
Apply statistical and machine learning techniques to solve complex business problems.
Build, validate, tune, and deploy predictive models.
Develop dashboards, reports, and visualizations to communicate insights to business stakeholders.
Work closely with data engineers to develop and optimize data pipelines required for analytics and AI solutions.
Translate business requirements into analytical models, experiments, and measurable outcomes.
Evaluate model performance using appropriate statistical and machine learning metrics.
Identify opportunities to leverage AI, Generative AI, and automation to improve business processes.
Collaborate with engineering, product, business, and technology teams throughout the AI/ML development lifecycle.
Document methodologies, assumptions, models, results, and recommendations.
Present complex analytical findings and AI insights clearly to both technical and non-technical stakeholders.

Required Technical Skills
5+ years of experience in Data Science, Data Analytics, Machine Learning, or a related field.
Strong hands-on experience with Python and data science libraries such as Pandas, NumPy, Scikit-learn, and Matplotlib.
Strong knowledge of SQL and experience querying and analyzing large datasets.
Experience with Exploratory Data Analysis (EDA) and statistical analysis.
Strong understanding of Machine Learning algorithms, including regression, classification, clustering, and ensemble methods.
Experience with model training, validation, feature engineering, hyperparameter tuning, and performance evaluation.
Experience with AI/ML frameworks and tools.
Knowledge of Generative AI, LLMs, prompt engineering, embeddings, and RAG is highly preferred.
Experience working with cloud platforms such as Azure, AWS, or Google Cloud Platform.
Experience with data visualization tools such as Power BI, Tableau, or similar.
Knowledge of data preprocessing, data quality, and data transformation techniques.
Familiarity with Git, CI/CD, and Agile development practices.