Job Description:
• Hands on design, development, deployment, and maintenance of data analytics, advanced analytics, data science and AI solutions.
• Lead end-to-end delivery of data science & advanced analytics engagements.
• Translate complex business problems into analytics and machine learning solutions.
• Perform diagnostic analytics / causal / driver analysis for complex business problems
• Develop predictive, prescriptive, and optimization models for enterprise use cases.
• Conducts exploratory data analysis activities (for example, basic statistical analysis, hypothesis testing, statistical inferences).
• Creates machine learning models for tasks like customer segmentation, sales forecasting, and churn prediction.
• Applies techniques in causal inference and quasi-experimental design (e.g., matching, ITS) to determine the incremental effect of programs and interventions.
• Design and implement AI-enabled analytics solutions, including forecasting, customer and revenue analytics, risk and anomaly detection, in-process intelligence and optimization.
• Selects appropriate modeling techniques for complex problems with large scale, multiple structured and unstructured data sets.
• Performs model assessment & optimization.
• Applies best practice techniques for model testing and tuning to assess accuracy, fit, validity, and robustness for multi-stage models and model ensembles.
• Writes queries (BigQuery, SQL …) and scripts to gather, clean, and pre-process raw structured and unstructured data for data science tasks.
• Write scripts to automate data cleaning and fusion processes.
• Helps manage, maintain, and improve client data sources.
• Works with data engineering to deploy scripts and models as reliable pipelines.
• Solid understanding of data manipulation and database technologies (SQL, NoSQL).
• Strong knowledge of machine learning algorithms and libraries (e.g., Scikit-Learn, TensorFlow, PyTorch).
• Build reusable analytics accelerators, frameworks, and industry solutions.
• Develop solutions around AI-driven BI, Agentic AI applications, AI-assisted data management, GenAI / LLM based innovative solutions etc.
• Create Proof-of-Concepts, Prototypes for innovative solutions.
• Work closely with client stakeholders including CFO, FP&A, operations, and IT leadership.
• Mentor and lead teams of data analysts, data engineers, and data scientists.
• Facilitate workshops on data strategy, analytics roadmap, and AI adoption.
• Serve as a trusted advisor to clients on data-driven decision making.
• Ability to establish and maintain strong working relationships with external and internal IT partners
• Support sales and business development activities including proposals and solution architecture.
• Present insights and recommendations to senior executives and business leaders.
• Ability to manage multiple projects / clients at the same time and work in an agile fashion.
• Performing change management support for stakeholders.
Requirements:
• 10+ years of experience in data analytics, data science, advanced analytics, or machine learning.
• 8+ years of experience delivering analytics solutions in a consulting or advisory environment.
• Strong expertise in diagnostic analytics, statistical modeling, machine learning, predictive analytics, and optimization techniques.
• Highly efficient in Python or R and comfortable with SQL.
• Hands-on experience with tools such as Python (pandas, scikit-learn, PyTorch, TensorFlow), SQL, Data science platforms (Databricks, Snowflake, AWS, Azure, GCP).
• Experience building production-grade ML pipelines and MLOps workflows.
• Must thrive when presenting complex analyses to non-technical stakeholders
• Experience applying statistics, experimental design, and causal inference through professional experience
• Experience developing end-to-end machine learning solutions through professional experience
• Ability to translate business problems into quantitative models and analytics solutions.
• Strong executive communication and presentation skills.
• Proven experience in enterprise analytics transformation or AI adoption programs.
• Experience in Generative AI and LLM-based applications.
• Experience with Retrieval Augmented Generation (RAG), AI copilots for analytics, and Agentic AI workflows.
• Familiarity with modern data architectures (data lakehouse, data mesh and medallion architecture).
• Experience in financial analytics, FP&A, or operational analytics is a plus.
• Experience in CRM, ERP, FP&A related technologies, systems and platforms is a plus.
• Advanced degree in Data Science, Statistics, Computer Science, Economics, Applied Mathematics, or other quantitative fields.
Benefits:
• Health insurance packages
• Wellness programs
• One-on-one coaching program
• Career development opportunities