About the Role:

KiE Square Analytics is seeking a results-driven, analytical Data Science Consultant with 3 - 4 years of hands-on experience to join our data and analytics practice. In this role, you will bridge the gap between complex statistical modelling and strategic business decisions. You will work directly with business stakeholders and cross-functional teams to translate business problems into data science solutions, build scalable Machine Learning models, and deliver actionable insights.

Key Responsibilities:

- Client & Stakeholder Engagement: Collaborate with business clients to understand pain points, translate requirements into technical specifications, and present data-driven recommendations clearly to non-technical audiences.

- Exploratory Data Analysis (EDA): Clean, preprocess, and analyse large structured and unstructured datasets to identify patterns, trends, and anomalies.

- Predictive & Prescriptive Modelling: Design, build, validate, and deploy ML models (e.g., classification, regression, clustering, time-series forecasting, NLP) to solve business challenges.

- Dashboarding & Storytelling: Create clear, impactful visualisations and executive-ready dashboards using BI tools to communicate findings effectively.

- Code Optimisation & Version Control: Write production-quality, reproducible Python/R code, maintaining version control best practices and participating in code reviews.

- Cross-Functional Collaboration: Partner closely with Data Engineers, Product Managers, and Solution Architects to integrate algorithms into client workflows or production pipelines.

Technical Expertise:

- Programming: Advanced proficiency in Python, including libraries like pandas, NumPy, scikit-learn, statsmodels, and matplotlib/seaborn.

- Database & SQL: Strong proficiency in writing complex SQL queries to extract, join, and manipulate data from relational databases (e.g., PostgreSQL, MySQL, Snowflake, BigQuery).

- Machine Learning: Solid background in supervised and unsupervised learning techniques (e.g., Random Forests, XGBoost, Logistic Regression, K-Means, Decision Trees).

- Data Visualisation: Experience building dashboards in Power BI, Tableau, or Looker.

- Tools & Environment: Familiarity with Git, Jupyter Notebooks, and cloud environments (AWS, GCP, or Azure).

Preferred Skills:

- Familiarity with LLMs, Generative AI APIs (e.g., OpenAI, Anthropic), or Retrieval-Augmented Generation (RAG) applications.

- Understanding of ML lifecycle frameworks (MLflow, Airflow) and MLOps fundamentals.

- Domain knowledge in industry verticals such as Retail/E-commerce, Healthcare, Financial Services, or Supply Chain.

Education:

- Bachelor's or Master's degree in Data Science, Computer Science, Statistics, Applied Mathematics, Economics, Engineering, or a related quantitative field.