Senior Data Analyst – Predictive Analytics, Forecasting
Kaufman Rossin
full-time
Posted on:
Location Type: Hybrid
Location: Bengaluru • India
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Job Level
About the role
- Build, validate, and deploy cross-sell prediction models using client engagement data, service utilization patterns, and industry benchmarks
- Develop time-series forecasting models for expense projection, cashflow forecasting, and revenue prediction across service lines
- Implement model monitoring, retraining pipelines, and performance tracking to ensure accuracy over time
- Apply advanced machine learning techniques to improve model performance and business outcomes
- Explore and implement LLM applications for client data analysis, proposal generation insights, and automated reporting
- Evaluate opportunities for RAG (Retrieval Augmented Generation) systems using firm knowledge bases
- Stay current on AI/ML advancements and recommend practical applications for professional services
- Design clustering and segmentation models to identify client growth potential and engagement patterns
- Analyze historical engagement data to uncover patterns driving successful client relationships and revenue expansion
- Partner with Business Intelligence team to translate models into actionable business recommendations and insights
- Present findings to firm leadership in clear, business-oriented terms
- Document methodologies and create reproducible workflows for model handoffs
- Establish best practices for model development, validation, and deployment
- Evaluate and recommend ML tools, platforms, and approaches as our analytics capabilities grow
Requirements
- 5-8 years of professional experience in data science, with at least 3 years building production ML models
- Proven track record of deploying forecasting models (time-series, regression) that drove business decisions
- Experience with customer segmentation, propensity modeling, or similar predictive analytics
- Demonstrated ability to work autonomously with minimal technical oversight
- Expert-level proficiency in Python or R for ML/statistical modeling (scikit-learn, XGBoost, Prophet, statsmodels, etc.)
- Strong SQL skills for data extraction and feature engineering
- Experience with model validation techniques (cross-validation, A/B testing, backtesting)
- Familiarity with MLOps practices: version control (Git), experiment tracking, model monitoring
- Familiarity with LLMs and Generative AI and eagerness to implement GenAI solutions - exposure to OpenAI API, LangChain, or similar frameworks preferred
- Experience in professional services, B2B SaaS, or similar client-centric industries preferred
- Ability to translate business problems into ML frameworks and vice versa
- Strong communication skills - can explain complex models to non-technical stakeholders
- Experience with BI platforms (Domo, Tableau, Power BI)
- Knowledge of CRM systems and APIs (Salesforce, HubSpot)
- Cloud platform experience (AWS SageMaker, Azure ML, GCP Vertex AI)
- Familiarity with accounting/finance metrics and professional services business model
Benefits
- Work-Life Balance
- People First Company
- Hybrid work policy
- Working directly with peers in the US
Applicant Tracking System Keywords
Tip: use these terms in your resume and cover letter to boost ATS matches.
Hard Skills & Tools
machine learningtime-series forecastingregression modelingcustomer segmentationpropensity modelingPythonRSQLmodel validationMLOps
Soft Skills
autonomous workcommunicationbusiness problem translationpresentation skills