Consultant  Data Science

Job highlights

• 4-7 years experience as Data Science consultant with 2+ years implementing Azure solutions; skilled in Azure Data Factory, Spark, ML model development, MLOps, and data engineering tools
• Execute delivery tasks for cloud modernization, implement data and ML components, collaborate across teams, follow coding standards, support documentation and testing, and apply Azure technologies to optimize solutions

Skill: Consultant – Data Science

Exp: 4 to 7 years

Location: Hyderabad

Shift:  Flexible to work in Shifts

As a Consultant, you contribute to technical delivery across modernization and cloud transformation projects. You work under the guidance of senior consultants and architects, applying strong handson skills to implement solutions that align with established standards. You build subjectmatter expertise while strengthening delivery consistency and technical execution.

Responsibilities

       Execute delivery tasks that support solution architectures and modernization priorities.

       Implement application, data, or cloud components based on established technical designs.

       Collaborate with team members across App Innovation, Cloud Platform, and Data & AI to ensure integrated delivery.

       Follow delivery frameworks, coding standards, and quality checks established for the project.

       Participate in design walkthroughs and apply feedback to improve technical execution.

       Collaborate with architects, project managers, and client stakeholders to drive successful outcomes.

       Support junior team members by sharing knowledge and best practices.

       Support documentation, testing, and validation activities aligned to delivery needs.

       Provide status updates, surface risks, and collaborate with leads to maintain execution quality.

       Apply current Azure technologies and Microsoft frameworks to optimize solutions for maintainability, performance, and cost efficiency.

       Contribute to reusable assets and internal frameworks as assigned.

       Manage workload and commitments to meet utilization goals while maintaining architectural quality across engagements.

       Invests in internal thought leadership, culture of feedback, knowledge-sharing, and/or Intellectual Property development opportunities, including communities of practice and internal initiatives.

       Delivery and Consulting Excellence

       Build trust with managers and product owners, influencing priorities and roadmaps in ways that support the client’s long-term interests.

       Support effective Agile practices by ensuring a clear Definition of Done, and data-informed course corrections based on telemetry and feedback.

       Demonstrated ability to leverage AI and emerging technologies to improve productivity, enhance decision-making, streamline delivery processes, and drive measurable business value. Commitment to continuous learning and adoption of new digital capabilities is required.

Requirements

       2 or more years of relevant experience implementing technical solutions on Azure.

       Two years of experience in data science, machine learning, or advanced analytics, preferably in consulting or client-facing settings.

       Demonstrated ability to build and deploy data and ML components on Azure or comparable cloud environments.

       Proficiency in one or two of the following areas:

o   Data Engineering & Integration – Spark, Azure Data Factory, Synapse, Delta Lake, or Data Lake Storage.

o   Machine Learning – model development, feature engineering, validation, and tuning using Python (pandas, NumPy, scikit-learn, PySpark).

o   MLOps & Automation – MLflow, Azure ML pipelines, CI/CD concepts, or model monitoring.

o   Analytics & Visualization – Power BI, Plotly, Seaborn, Matplotlib for communicating insights.

o   Cloud & AI Foundations – exposure to Azure Databricks, Fabric, or Prompt Flow; basic understanding of agentic frameworks (Semantic Kernel, AutoGen).

       Familiarity with data modeling, experiment design, and statistical inference concepts.

       Knowledge of modern development tools such as GitHub, Copilot, and version control workflows.

       Exposure to data governance, metadata management, or Responsible AI practices.

       Prior hands-on work with Databricks Unity Catalog or Microsoft Fabric.

       Relevant Azure or Databricks certifications in Data, AI, or Architecture.

       Technical foundation in one Azure domain (Application Innovation, Cloud Platform, or Data & AI)

       Demonstrated ability to execute assigned tasks within application, cloud, or data domains.

       Experience working within delivery teams and following established technical processes with attention to detail.

       Effective communicator able to work collaboratively across project teams.

       Exposure to hybrid or multi-cloud environments is a plus