Top Remote Entry Level Data Science Jobs in Boston, MA

Develop forecasts, machine learning models, and optimization algorithms for renewable energy and battery storage assets. Analyze large energy-market datasets, build simulations, deploy production-grade data science workflows, create dashboards, and establish asset-optimization metrics. The role collaborates with engineering, operations, and energy markets teams to support real-time operations, market strategies, and business development.

Designs challenging competitive data science and machine learning tasks, reproducible execution environments, reference solutions, automated tests, and evaluation criteria. Uses Python, statistical modeling, mathematical reasoning, and debugging to assess AI coding agents, identify data leakage and implementation flaws, validate solution correctness, and document methodologies. Requires a verifiable Kaggle Expert-level profile, strong practical programming skills, and the ability to independently test and explain technical solutions.

Analyze large and complex datasets to support product, marketing, and business decisions. Refine and optimize language models through fine-tuning and retraining, develop predictive and machine learning models, manage text data, write SQL queries, and create production-ready code. Collaborate with product, engineering, DevOps, and MLOps teams to integrate models into BetterHelp products and improve user experiences.

Create reproducible data science and machine learning tasks with reference solutions, robust tests, and evaluation criteria. Investigate coding-agent failures, identify errors, data leakage, and flawed assumptions, then refine tasks accordingly. Document requirements, solution reasoning, and verification steps. The role requires competitive data science experience, mathematical reasoning, programming ability, and practical knowledge of machine learning and quantitative modeling.

Develop and evaluate foundational deep learning methods, including transformers, embeddings, and neural networks, for credit modeling and loss prediction. Integrate models into ML pipelines, apply representation learning to financial data, and build specialized AI agents for data science and business workflows. Collaborate with technical and product teams, communicate findings, mentor colleagues, and measure model impact on customer and business outcomes.

Build and validate forecasting, optimization, anomaly detection, and measurement models using complex customer data. Perform exploratory analysis, experiments, backtesting, and error analysis; partner with ML and data engineers to productionize models, features, evaluation pipelines, and monitoring. Translate business questions into analytical recommendations and communicate findings to technical and non-technical stakeholders. The role is hybrid in New York City or remote in the U.S. with monthly travel to New York.

Analyze large datasets, identify meaningful patterns, develop machine learning models, and deliver actionable insights for business decisions. Collaborate with cross-functional teams to understand data needs, communicate findings clearly to stakeholders, and stay current with advances in data science and AI technologies.

Supports the design, validation, and operationalization of predictive, machine learning, and AI models. Analyzes structured and unstructured data, develops algorithms, validates outcomes, creates visualizations, and communicates findings to stakeholders. Collaborates cross-functionally to integrate solutions and prepares model pipelines for business use. The role also involves statistical analysis, NLP, exploratory data analysis, feature engineering, and research into emerging technologies.

Develop and deploy mineral exploration instruments, sensor systems, data acquisition methods, and proprietary software. Integrate geospatial, geophysical, geochemical, geological, and geographic data; build statistically rigorous and physics-based models; identify ore-forming anomalies; and generate 2D and 3D exploration predictions. Collaborate with data scientists, engineers, geoscientists, vendors, and external partners to design field programs, deploy sensors, reduce uncertainty, and guide exploration decisions.

Own Junction’s data science research, statistical modeling, experimentation, and evaluation strategy across diagnostics, clinical workflows, and AI-enabled products. Build predictive and decision-support models, establish measurement and quality frameworks, partner with engineering to deploy reliable systems, and communicate findings to technical and executive stakeholders. The role combines strategic roadmap ownership with hands-on analytical work in a high-ambiguity startup environment.