Data Scientist

Description GSPANN is hiring a Data Scientist to build and deploy advanced data science and machine learning solutions. The role focuses on transforming complex data into actionable insights and driving measurable business impact through AI and analytics.

Role and Responsibilities

Identify opportunities to apply data science across multiple business functions.

Build predictive and prescriptive models to address use cases across commercial, supply chain, and operations domains.

Clean, analyze, and visualize large datasets to uncover key trends, patterns, and business drivers.

Partner with business stakeholders to frame problems clearly and deliver actionable, data-driven insights.

Deploy models into production and continuously monitor their performance and effectiveness.

Integrate Machine Learning (ML) models into Artificial Intelligence (AI)–driven applications by working closely with AI Data Engineers and software development teams.

Document methodologies, assumptions, and results, and communicate findings effectively to both technical and non-technical audiences.

Support the scaling of enterprise-wide AI capabilities through reusable frameworks and best practices.

Align data science initiatives with critical business priorities and strategic goals.

Ensure strong adoption and measurable impact by collaborating closely with cross-functional stakeholders.

Deliver analytical solutions that demonstrate clear, quantifiable business outcomes.

Apply rigor in experimentation, validation, and model evaluation techniques.

Explore datasets proactively to identify new insights, risks, and growth opportunities.

Solve complex business problems by combining creativity, critical thinking, and analytical depth.

Skills and Experience

Demonstrate strong analytical and problem-solving skills.

Preprocess and optimize structured and unstructured datasets to make them Large Language Model (LLM)–ready.

Select and apply appropriate prompt engineering techniques, agentic frameworks, and emerging AI technologies to address business challenges.

Bring proven experience in data science, statistical modeling, and machine learning.

Develop solutions using strong programming skills in Python or R, with hands-on experience in libraries such as Scikit-learn, XGBoost, PyTorch, TensorFlow, Pandas, and NumPy.

Create compelling data visualizations and narratives using tools such as Power BI, Tableau, or Python-based dashboards (Dash, Streamlit).

Query, transform, and manage data efficiently using Structured Query Language / NoSQL (SQL / NoSQL) across structured and unstructured data sources.

Translate business processes and requirements into scalable, analytical, and AI-driven solutions.

Design and maintain data pipelines using Extract, Transform, Load / Extract, Load, Transform (ETL / ELT) processes and orchestration tools, including Microsoft Fabric.

Apply Machine Learning Operations (MLOps) practices and work confidently across cloud platforms such as Microsoft Azure and Amazon Web Services (AWS).

Leverage cloud-based AI services, including Azure AI, Copilot Studio, and AI Foundry, as an added advantage.

Develop responsible and trustworthy AI solutions that align with enterprise governance and compliance standards.

Define and implement integration points across multiple products to ensure End-to-End (E2E) application flow.

Utilize modern AI approaches such as Retrieval-Augmented Generation (RAG), Model Context Protocol (MCP), and multi-agent architectures in AI applications.

Communicate effectively as a fluent English speaker, both verbally and in writing.

Hold a Master’s degree in Data Science, Statistics, Mathematics, Computer Science, or a related field; certifications in machine learning or analytics are a strong plus.