Data Science Consultant

Job Overview

The Data Science Consultant sits within Stellar’s Solutions practice. The team’s mandate is simple: turn data into decisions. We deliver customized, high-value insights through data analysis and modeling, acting as the bridge between what a client’s business needs and what the technology can actually do — and we hold ourselves to a high bar on the quality of what goes out the door.

Responsibilities and Duties

• Lead the delivery of a broad range of projects and services across Value Creation Insights and other engagements that carry significant business and/or technical complexity.
• Act as the translator between business and technical requirements — shaping project scope, defining customization needs, and packaging outcomes so they land clearly with business executives.
• Support the Executive Team and Sales in building proposals that require a tailored approach, and align resourcing with the delivery team accordingly.
• Build custom analytical approaches for insights that fall outside Stellar’s standard data pipelines — this means hands-on coding in Python/SQL, and continuously checking and improving model, classification, and clustering performance while correcting for content bias.
• Own quality control across both standard data pipelines (from data lineage and sourcing through to insight generation) and custom-built approaches — reviewing outputs from both a business and a technical lens, and iterating until the outcome is right.
• Translate findings into clear, executive-ready PowerPoint presentations and Power BI dashboards.
• Represent Stellar in client read-outs, presenting results directly to client stakeholders.
• Manage handovers to clients and stay accountable for their ongoing success with the solution delivered.
• Support the Value Creation Insights Product Lead on broader solution development work alongside day-to-day client delivery.
• Follow Stellar’s information security policies, procedures, and standards within the scope of the role, and support our ISO certification and ISMS commitments.
• Proactively identify and push for improvements, building the case for their inclusion in the product/solutions roadmap.

Education, Required Skills and Experience

• PhD or Master’s degree in Computer Science, Data Science, Applied Mathematics, Statistics, Econometrics, or Engineering, with at least one year of business curriculum and one year of data/analytics/statistics curriculum.
• 3+ years of professional experience, ideally in strategy consulting, spanning a broad range of projects — with at least one year specifically in Growth, Strategy, and Marketing.
• Strong command of quantitative methods for data analysis.
• Demonstrated track record building robust analytics solutions, not just prototypes.
• Ability to quickly get to grips with complex business topics, structure problems clearly, form and test hypotheses, and carry a solution through to execution.
• Strong communication skills — structured, empathetic, and emotionally intelligent, especially in client-facing settings.
• Working knowledge of:
  • Supervised and unsupervised learning model evaluation
  • Data transformation
  • Data visualization (charts and dashboards)
  • Core Python data science packages (pandas, numpy, scipy, scikit-learn, etc.)
  • NLP packages such as spaCy
  • Transformer/LLM-based NLP pipelines
• Working knowledge of software engineering fundamentals: Python, SQL and databases, Git, API/web service development (e.g., FastAPI).
• Working familiarity with MLOps, SSH, Linux terminal, Bash, UI development (app or web-based), system architecture, and agile methodologies.
• Fluency in English, written and spoken.
• A respectful team player with high ethical standards.

Preferred Skills and Experience (Upside)

• Due diligence experience in a deal/transaction context.
• Multilingual.
• A track record of high achievement — sports, arts, games, academics, competitions, awards — any domain.
• Knowledge of Microsoft Azure DevOps Services.
• Experience with deep learning libraries such as TensorFlow or PyTorch.
• Hands-on experience with cloud-based NLP/LLM platforms — OpenAI’s GPT/ChatGPT, Meta’s Llama, IBM Watson NLP, Amazon Comprehend, Azure NLP, Google NLP, BERT, Gemini, or similar.
• Knowledge of containers and orchestration (Docker, Docker Compose, Kubernetes).