Data Scientist

Company Description

EcoRatings is a sustainability-focused company founded by experienced entrepreneurs with a successful prior exit. Leveraging enterprise-grade Generative AI and blockchain technologies, EcoRatings assists companies, Web3 infrastructures, governments, and agencies in achieving their sustainability objectives. At the core of its work is the open-source sustaining.ai foundation model, fine-tuned for sustainability and ESG-related applications, including Retrieval Augmented Generation (RAG) and autonomous AI agents for carbon measurement, reporting, and validation. Additionally, EcoRatings has developed a proprietary blockchain to manage Carbon Credits. The company operates with innovation and holds patents pending in the United States.

About this Job

EcoRatings is seeking an exceptional Senior Data Scientist to drive the development and deployment of advanced machine learning models that power our AI-driven sustainability intelligence platform.

As a key member of the Intelligence & Data track, you will transform complex environmental and enterprise data into actionable insights through sophisticated statistical modeling and machine learning. This role is ideal for a data scientist who excels at translating business challenges into data science solutions and thrives in the intersection of AI innovation and real-world impact.

Responsibilities

Model Development & Deployment: Design, develop, and deploy advanced machine learning models including predictive analytics, classification, and recommendation systems tailored to sustainability and ESG metrics.

Generative AI Integration: Collaborate with AI Engineers to enhance RAG (Retrieval-Augmented Generation) pipelines, fine-tune LLMs, and implement prompt engineering strategies for domain-specific applications.

Feature Engineering: Work closely with Data Engineers to identify, extract, and engineer features from diverse enterprise data sources (Oracle ERPs, SQL databases, environmental datasets).

Statistical Analysis: Conduct rigorous statistical analysis and hypothesis testing to validate model assumptions and ensure robust, interpretable results for enterprise clients.

Model Evaluation & Optimization: Establish comprehensive model evaluation frameworks, including A/B testing, cross-validation, and performance monitoring to continuously improve model accuracy and reliability.

Data Science Pipeline: Build end-to-end ML pipelines from data preprocessing and model training to deployment and monitoring using MLOps best practices.

Insight Generation: Translate complex analytical findings into clear, actionable insights and compelling data visualizations for both technical and non-technical stakeholders.

Research & Innovation: Stay abreast of cutting-edge developments in AI/ML, particularly in sustainability analytics, NLP, and generative AI, and identify opportunities for innovation within EcoRatings platform.

Cross-functional Collaboration: Partner with Full Stack developers, Data Engineers, and product teams to integrate ML models seamlessly into production applications.

Qualifications

Education: Bachelor's or Master's degree in Data Science, Computer Science, Statistics, Mathematics, Physics, or a related quantitative field.

Technical Proficiency: Expert-level knowledge of Python and ML libraries (scikit-learn, TensorFlow, PyTorch, XGBoost). Strong command of SQL for data manipulation and analysis.

Machine Learning Expertise: Proven experience building and deploying supervised and unsupervised learning models, including regression, classification, clustering, and time-series forecasting.

Generative AI Experience: Hands-on experience with LLMs, vector databases (Pinecone, Weaviate), embeddings, and retrieval-augmented generation techniques.

Statistical Foundation: Solid understanding of statistical concepts including probability theory, experimental design, hypothesis testing, and causal inference.

MLOps & Deployment: Familiarity with model deployment frameworks (MLflow, Kubeflow, SageMaker) and containerization technologies (Docker, Kubernetes).

Cloud Platforms: Experience with AWS (SageMaker, Lambda, S3) or Azure ML services for scalable model training and deployment.

Data Visualization: Proficiency in creating impactful visualizations using tools like Tableau, PowerBI, Plotly, or Matplotlib/Seaborn.

Domain Knowledge: (Preferred) Understanding of ESG metrics, sustainability frameworks, or environmental data analytics.

Communication Skills: Exceptional ability to communicate complex technical concepts to diverse audiences, including executives and non-technical stakeholders.

Problem Solving: Strong analytical mindset with the ability to break down ambiguous business problems into structured data science solutions.