Green Ocean Property Management
Junior AI engineer
Green Ocean Property Management, Newton, Massachusetts, United States, 02165
Role Overview
Green Ocean Open Management is hiring a Junior AI Engineer to join our core AI & Quantitative Engineering division. You will help engineer and optimize ML systems for high-frequency financial prediction, risk analytics, and algorithmic decision support. The role requires strong data-centric programming skills, an understanding of ML/DL fundamentals, and an appetite to apply AI in capital markets and sustainable investing frameworks.
Core Responsibilities
Implement and maintain ML pipelines for financial time-series modeling, credit scoring, sentiment analysis, and alpha signal generation. Develop and validate predictive models using libraries such as scikit-learn, XGBoost, PyTorch, or TensorFlow. Support feature engineering and model tuning using structured market data, alternative data sources, and NLP-extracted signals. Integrate model outputs with real-time financial dashboards or algorithmic trading engines. Work closely with data engineers and quant analysts to ensure reproducibility and scalability of models across backtesting and production environments. Perform error analysis and iterate on model robustness, fairness, and performance monitoring. Required Technical Skills
Solid programming foundation in Python; hands-on with Pandas, NumPy, matplotlib, joblib, etc. Experience in implementing supervised/unsupervised ML algorithms (logistic regression, decision trees, clustering, PCA, etc.). Understanding of time-series modeling concepts: stationarity, lag features, moving averages, autocorrelation. Exposure to version control systems like Git, and use of Jupyter, VS Code, or Colab for research workflows. Familiarity with SQL-based data retrieval and ETL operations for structured financial datasets. Preferred (Bonus) Qualifications
Familiarity with LLMs, prompt engineering, or APIs from OpenAI/HuggingFace (e.g., for financial document QA or semantic analysis). Understanding of financial instruments (equities, derivatives, fixed income), market microstructure, or portfolio theory. Experience with model deployment (Docker, FastAPI, Flask) or monitoring (Weights & Biases, MLflow). Cloud experience (AWS/GCP/Azure) for scalable model training or data warehousing. What You'll Gain
Hands-on work with production-grade financial ML systems under the guidance of quant and AI leads. Opportunity to work on green finance use cases, sustainable investing algorithms, and ESG-oriented NLP models. Cross-functional exposure across AI research, financial engineering, and operational deployment. Culture of continuous learning, internal research sprints, and access to cloud GPU environments.
Green Ocean Open Management is hiring a Junior AI Engineer to join our core AI & Quantitative Engineering division. You will help engineer and optimize ML systems for high-frequency financial prediction, risk analytics, and algorithmic decision support. The role requires strong data-centric programming skills, an understanding of ML/DL fundamentals, and an appetite to apply AI in capital markets and sustainable investing frameworks.
Core Responsibilities
Implement and maintain ML pipelines for financial time-series modeling, credit scoring, sentiment analysis, and alpha signal generation. Develop and validate predictive models using libraries such as scikit-learn, XGBoost, PyTorch, or TensorFlow. Support feature engineering and model tuning using structured market data, alternative data sources, and NLP-extracted signals. Integrate model outputs with real-time financial dashboards or algorithmic trading engines. Work closely with data engineers and quant analysts to ensure reproducibility and scalability of models across backtesting and production environments. Perform error analysis and iterate on model robustness, fairness, and performance monitoring. Required Technical Skills
Solid programming foundation in Python; hands-on with Pandas, NumPy, matplotlib, joblib, etc. Experience in implementing supervised/unsupervised ML algorithms (logistic regression, decision trees, clustering, PCA, etc.). Understanding of time-series modeling concepts: stationarity, lag features, moving averages, autocorrelation. Exposure to version control systems like Git, and use of Jupyter, VS Code, or Colab for research workflows. Familiarity with SQL-based data retrieval and ETL operations for structured financial datasets. Preferred (Bonus) Qualifications
Familiarity with LLMs, prompt engineering, or APIs from OpenAI/HuggingFace (e.g., for financial document QA or semantic analysis). Understanding of financial instruments (equities, derivatives, fixed income), market microstructure, or portfolio theory. Experience with model deployment (Docker, FastAPI, Flask) or monitoring (Weights & Biases, MLflow). Cloud experience (AWS/GCP/Azure) for scalable model training or data warehousing. What You'll Gain
Hands-on work with production-grade financial ML systems under the guidance of quant and AI leads. Opportunity to work on green finance use cases, sustainable investing algorithms, and ESG-oriented NLP models. Cross-functional exposure across AI research, financial engineering, and operational deployment. Culture of continuous learning, internal research sprints, and access to cloud GPU environments.