RAJEEV KRISHNA

Data Scientist

Bangalore Urban, Karnataka, India5 yrs 1 mo experience

Most Likely To SwitchHighly Stable

Key Highlights

Developed automation tools enhancing reporting efficiency.
Reduced pricing forecast error by over 45%.
Automated campaign processes, saving significant manual effort.

Stackforce AI infers this person is a Data Science and Analytics professional with experience in Fintech and Marketing sectors.

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Skills

Core Skills

Data ScienceMachine LearningBusiness AnalyticsAnalytics

Other Skills

AutomationCC#C++Data AnalysisDeep LearningEngineeringKPI DesignLinear RegressionMicrosoft ExcelMicrosoft OfficeMicrosoft WordPYTHONPandas (Software)Performance Monitoring

About

I’m an IIT Madras and IIM Calcutta graduate with 4+ years of experience in Data Science, Machine Learning, and Analytics across global firms like Goldman Sachs, PwC, and EXL. At Goldman Sachs, I developed automated analytics tools for multi-asset portfolios using Python, helping improve reporting efficiency and performance monitoring for global clients. At PwC, I built a pricing engine that reduced forecast error (MAPE) by over 45%. I've also worked on customer segmentation, time series forecasting (SARIMA, VAR), and computer vision projects involving CNNs and transfer learning. Core Skills: Python, Scikit-learn, SQL, Time Series Analysis, Clustering, Deep Learning (CNN, VGG19), Tableau, Financial Reporting, NLP (basics), Prompt Engineering (learning phase) I’m looking for impactful Data Science / ML / AI roles in Bengaluru or remote/hybrid setups. My ideal environment combines real-world data challenges, model deployment, and room for innovation in AI. If you're hiring or building something exciting in AI/ML, let’s connect!

Experience

5 yrs 1 mo

Total Experience

2 yrs 6 mos

Average Tenure

4 yrs

Current Experience

Goldman sachs

Associate - Asset and Wealth Management

May 2022 – Present · 4 yrs · Bengaluru, Karnataka, India · On-site

Developed Python-based automation tools to flag performance anomalies in multi-asset portfolios, improving historical tracking accuracy across client accounts
Led enhancements for report bundling processes using Python (Jupyter), streamlining performance documentation retrieved via Outlook into shared repositories
Analyzed top-level and account-level data using internal tools (Omega, Dashboard, Opal) to support client-level performance insights across OCIO and partnerships
Collaborated with cross-functional teams to deliver efficient fund reporting for clients, including handling paper portfolios and reconciliation tasks
Strengthened quality checks and alert systems for performance shifts, improving operational accuracy and speed in analytics reviews

PythonAutomationPerformance MonitoringData AnalysisData ScienceMachine Learning

Pwc india

Corporate Internship - Data Science & Pricing Analytics

Nov 2021 – May 2022 · 6 mos · Bengaluru, Karnataka, India · Remote

Designed an Intelligent Price Engine leveraging multiple input variables (discounts, quantities, region, inventory) to suggest optimal deal pricing
Conducted variable interaction experiments and regression tuning, reducing MAPE from 13% to 7% in pricing forecasts
Worked closely with stakeholders to ensure pricing recommendations aligned with business targets like win rate and margin improvement
Delivered a repeatable and scalable Python-based solution as part of margin optimization efforts for B2B pricing teams

PythonPricing StrategyRegression TuningData AnalysisData ScienceBusiness Analytics

Exl

Assistant Manager – Consultant I (Analytics)

Sep 2019 – Oct 2020 · 1 yr 1 mo · Bengaluru, Karnataka, India · On-site

🔹Digital Marketing Analytics
Automated campaign targeting processes across US & Canada, reducing manual effort by 20 hours/month
Designed KPI comparison tools between test and control groups, improving campaign effectiveness measurement time by 66%
Created business advisor segmentation models using engagement thresholds to support targeted messaging and retention campaigns
🔹 Investment Operations Support
Automated PDF table extraction from annual financial reports using C#, reducing manual effort by 90%
Enabled scalable extraction pipelines for downstream financial analysis, resulting in higher throughput and consistency across operations teams

AutomationKPI DesignData AnalysisC#AnalyticsData Science