Role Overview :

As a Data Scientist, you will work with business stakeholders, AI engineers, and domain experts to transform data into actionable insights and intelligent solutions. You will develop machine learning models, perform statistical analysis, and contribute to AI-driven products that create measurable business impact.

Key Responsibilities :

Data Science & Machine Learning :

- Analyze structured and unstructured data to identify patterns, trends, and business opportunities.

- Perform exploratory data analysis (EDA), feature engineering, and data preparation.

- Develop, evaluate, and optimize machine learning models for prediction, classification, clustering, and forecasting.

- Apply statistical techniques to solve business problems and validate model performance.

- Design and execute experiments to improve model accuracy and business outcomes.

AI Solution Development :

- Collaborate with AI Engineers, Data Engineers, and domain experts to build AI-powered solutions.

- Translate business requirements into scalable data science approaches.

- Contribute to Generative AI and advanced analytics initiatives where applicable.

- Document methodologies, model performance, and key findings.

Required Technical Skills :

- Strong programming skills in Python and SQL for data analysis, feature engineering, and machine learning.

- Strong understanding of Statistics, Probability, Linear Algebra, and Calculus as applied to machine learning and data science.

- Experience with Exploratory Data Analysis (EDA), data preprocessing, feature engineering, feature selection, and handling missing or imbalanced data.

- Good understanding of Supervised, Unsupervised, and Ensemble Machine Learning algorithms, including their assumptions, strengths, limitations, and appropriate use cases.

- Strong knowledge of Regression, Classification, Clustering, Time Series Forecasting, Dimensionality Reduction, Recommendation Systems, and Anomaly Detection techniques.

- Experience with Model Evaluation, Cross-Validation, Hyperparameter Optimization, Bias-Variance Trade-off, Feature Importance, Explainable AI (XAI), and Performance Metrics.

- Understanding of Statistical Inference, Hypothesis Testing, Probability Distributions, Sampling Techniques, Confidence Intervals, and A/B Testing.

- Experience translating business problems into analytical approaches and developing scalable, data-driven solutions.

- Working knowledge of Generative AI, Large Language Models (LLMs), Prompt Engineering, and Retrieval-Augmented Generation (RAG) is preferred.

Preferred Qualifications :

- Bachelor's or master's degree in computer science, Artificial Intelligence, Data Science, Statistics, Mathematics, Engineering, or a related field.

- 2 to 4 years of experience developing machine learning or data science solutions.

- Experience working on end-to-end data science projects in a business environment.

Nice to Have :

- Exposure to Generative AI, LLMs, RAG, or Agentic AI.

- Experience with Computer Vision or Natural Language Processing (NLP).

- Familiarity with cloud-based AI platforms.

- Knowledge of construction, engineering, manufacturing, or industrial domains.

- Participation in hackathons, research, Kaggle competitions, or open-source projects.

Soft Skills :

- Strong analytical and problem-solving skills, effective communication and collaboration, ownership mindset, adaptability, continuous learning, and a passion for innovation.