OBBU YASWANTH

ML Engineer and Analyst

Nizampet, Hyderabad, Telangana State 2+ yrs exp 91 · Outstanding

About

ML Engineer and Analyst with 2.5+ years of experience building production-grade financial models, algorithmic research systems, and forecasting pipelines across equity markets and energy commodities. Deep expertise in time-series forecasting, factor modelling, generative AI, LLM-powered research automation, and statistical signal generation. Adept at bridging quantitative finance with modern AI/ML to drive alpha, improve investment decision-making, and automate research workflows at scale.

Skills & Expertise (40)

Python Advanced

8.5/10

Years Exp

SQL Advanced

8.2/10

Years Exp

Deep Learning Advanced

8.0/10

Years Exp

AWS Advanced

8.0/10

Years Exp

Generative AI Advanced

8.0/10

Years Exp

Time-Series Forecasting Advanced

8.0/10

Years Exp

Model Optimisation Intermediate

7.5/10

Years Exp

NLP Intermediate

7.5/10

Years Exp

Docker Intermediate

7.5/10

Years Exp

ETL Design Intermediate

7.0/10

Years Exp

Power BI Intermediate

7.0/10

Years Exp

REST APIs GitHub GitHub Actions Advanced Excel EC2 Jupyter Notebook S3 Lambda SageMaker Alteryx MySql Postgresql ResNet LSTM ARIMA Prophet XGBoost LightGBM AutoML Autogluon Transformers Snowflake Sentiment Analysis GPT Claude API RAG Pipelines Vector Embeddings Computer Vision YOLO

Work Experience

FP&A Analyst - Data Science & AI Enablement

Sol-Millennium Medical Group

Jul 2026 - Present

Working on modernizing FP&A processes by leveraging Python, SQL, Snowflake, Power BI, and AI to improve financial planning and reporting workflows. Supporting the development of automated forecasting, scenario planning, and financial analytics solutions for revenue, COGS, inventory, operating expenses, and cash flow. Contributing to AI-enabled finance initiatives, including LLM-assisted reporting, variance analysis, and workflow automation. Collaborating with Finance, IT, and business stakeholders to build scalable data pipelines, dashboards, and governed analytics solutions. Gaining hands-on experience with enterprise finance systems, cloud data platforms, and modern FP&A technologies while contributing to digital transformation projects.

Research Associate – Quantitative Data & ML

Wood Mackenzie

Jul 2025 - Jul 2026

Architected scalable quantitative ML forecasting pipelines for global energy commodity datasets, enabling investment-grade price forecasting, supply-demand modelling, and market analytics across EMEA, APAC, and Americas. Developed and optimised Temporal Fusion Transformer (TFT) models for multivariate time-series forecasting of solar and wind energy demand, integrating weather signals, grid capacity data, and macroeconomic indicators as exogenous inputs. Conducted quantitative research on energy market fundamentals, translating large structured datasets into forward-looking investment signals and scenario analyses. Engineered automated ETL pipelines using Python, SQL, Snowflake, and REST APIs, processing millions of data points daily for ingestion, transformation, and feature engineering across market datasets. Deployed production ML models on AWS (SageMaker, Lambda, S3, EC2) with CI/CD via Docker and GitHub Actions, ensuring model versioning, reproducibility, and low-latency inference. Performed advanced feature engineering lag features, rolling statistics, Fourier transforms, calendar effects — improving model stability and out-of-sample forecasting performance. Designed anomaly detection and data drift monitoring frameworks, reducing data quality incidents across the financial and energy market data.

ML Engineer – Investment Research

Winbold Capital Management

May 2024 - May 2025

Led end-to-end development of a proprietary stock analytics platform using SQL, ML models, LLMs, and RAG pipelines, reducing equity analyst research time by 25% and improving investment signal quality. Built multi-factor alpha models incorporating price momentum, earnings revision, valuation, and sentiment factors to generate ranked investment signals across 500+ equities, improving portfolio selection accuracy. Designed and deployed Generative AI systems using GPT-based LLMs and Claude API for automated earnings call summarisation, financial report analysis, and investment thesis generation, cutting research turnaround by 30%. Implemented RAG pipelines with vector embeddings and semantic search over 10-Ks, analyst reports, and financial news to produce context-aware investment insights at scale. Built agent-based AI workflows to automate multi-step financial research tasks — data retrieval, comparative analysis, and report generation with minimal human intervention. Applied AutoML (AutoVIML, AutoGluon) and developed a YOLO-based CV model to detect candlestick chart patterns and technical formations from historical OHLCV price charts.

Project Coordinator and Analyst Intern (FinOps)

AAvishkar Oral Strips Pvt. Ltd.

Jan 2023 - Sep 2023

Conducted financial analysis on operational datasets using SQL, Excel, and Power BI, identifying supply chain inefficiencies and recommending reallocation strategies that reduced costs by 25%. Built DCF and scenario analysis models in Excel to evaluate capex decisions, assess project viability, and support management in strategic investment planning. Developed executive Power BI dashboards tracking revenue KPIs, budget variance, cost centre performance, and working capital, improving C-suite financial visibility. Automated financial reporting workflows using Python and Excel macros, reducing manual effort by 40% and improving accuracy of monthly management accounts. Prepared variance analyses and business performance summaries for senior management, translating raw financial data into structured investment and operational insights.