Job Title: Principal Data Scientist Location: Juno Beach, FL onsite day 1 Job Type: Contract Duration: 12 months with possible extension Experience: 10+ years (or similar senior-level experience in automation)
Domain & Advanced Expectations:
Experience working with large, messy datasets and modern data technologies
Strong analytical mindset with ML and LLM exposure as a plus
Proven time-series forecasting experience
Candidates from energy, utility, or renewable sectors preferred
Experience with ARIMA/SARIMAX/Prophet/LSTM models
Evidence of weather-dependent forecasting projects
Experience deploying production-grade forecasting systems
Required Skills & Qualifications:
9+ years of experience as a Data Scientist / Data Analyst
Strong proficiency in Python for data manipulation and analysis (Pandas, NumPy, SciPy)
Solid understanding of data cleaning, transformation, and feature engineering
Experience with SQL (PostgreSQL, MySQL, BigQuery, Snowflake, etc.)
Familiarity with data visualization tools (Matplotlib, Seaborn, Plotly, Power BI/Tableau)
Strong understanding of statistics and data analysis fundamentals
Experience working with APIs and external data sources
Strong problem-solving and communication skills
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 for data extraction and preparation
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 as required
Experiment with and support LLM-based solutions (prompting, embeddings, APIs)
Ensure data quality, reliability, and proper documentation
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 models: regression, classification, clustering, time series
Exposure to LLMs and Generative AI (OpenAI / Azure OpenAI APIs)
Prompt engineering
Embeddings and vector databases (FAISS, Pinecone, Chroma)
Experience with NLP or text analytics
Knowledge of MLOps basics (model versioning, monitoring)
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