Principal Data Science Architect

We are seeking a Data Scientist with 9 or more years of hands on experience in data cleaning, transformation, and analysis using Python. The ideal candidate is comfortable working with large, messy datasets, has exposure to modern data technologies, and brings a strong analytical mindset. Experience with machine learning and LLMs is a strong plus.

• Data scientists with proven time-series forecasting portfolios
• Candidates from energy, utility, or renewable sectors
• Profiles showing ARIMA/SARIMAX/Prophet/LSTM experience
• Evidence of weather-dependent forecasting projects
• Production deployment of operational forecasting systems

Key Responsibilities

Clean, preprocess, and transform structured and unstructured data using Python

Perform exploratory data analysis (EDA) to uncover insights and trends

Build reusable data pipelines and feature engineering workflows

Work with SQL and/or cloud based data warehouses to extract and prepare data

Collaborate with stakeholders to translate business problems into data driven solutions

Develop and maintain analytical models and dashboards

Apply basic to intermediate machine learning techniques where applicable

Experiment with and support LLM based solutions (prompting, embeddings, APIs) as needed

Ensure data quality, reliability, and documentation

Required Skills & Qualifications

9 years of experience as a Data Scientist / Data Analyst

Strong proficiency in Python for data manipulation and analysisPandas, NumPy, SciPy

Solid understanding of data cleaning, transformation, and feature engineering

Experience with SQL (PostgreSQL, MySQL, BigQuery, Snowflake, etc.)

Familiarity with data visualization toolsMatplotlib, Seaborn, Plotly, or Power BI/Tableau

Understanding of statistics and data analysis fundamentals

Experience working with APIs and external data sources

Strong problem solving and communication skills

Modern / Latest Tech Stack (Preferred)

Python (3.x)

Pandas, NumPy, Scikit learn

Jupyter, VS Code

Git / GitHub

Cloud platforms: AWS / Azure / Google Cloud Platform

Data tools: Airflow, dbt, Spark (basic exposure)

Containerization: Docker (nice to have)

Good to Have

Hands on experience with Machine Learning modelsRegression, classification, clustering, time series

Exposure to LLMs and Generative AIOpenAI / Azure OpenAI APIs

Prompt engineering

Embeddings, vector databases (FAISS, Pinecone, Chroma)

Experience with NLP or text analytics

Knowledge of MLOps basics (model versioning, monitoring)