Akanksha Wagh

Data Scientist

Datadog

Aug 2025 - Current (11 months)

• Built scalable time-series anomaly detection and forecasting models for distributed observability metrics, enabling proactive monitoring across cloud infrastructure and improving detection accuracy and operational reliability. • Designed advanced experimentation frameworks including holdouts, bandits, and synthetic controls to evaluate product features and model performance across customer segments. • Developed large-scale telemetry data pipelines using Python and PySpark, supporting near real-time analytics and reducing data latency for observability insights. • Implemented zero-shot anomaly detection techniques across new metrics, reducing onboarding time by 35% and enabling faster monitoring for newly deployed infrastructure components

Data Scientist – Retail & Marketing

Compass Group

Feb 2024 - May 2025 (1 year 4 months)

• Built GenAI-powered analytics agent using LLMs to automate KPI tracking, weekly reporting, and business insights generation across marketing and retail stakeholders. • Developed customer segmentation and demand forecasting models using clustering and regression techniques, improving inventory planning efficiency and reducing stock imbalances. • Applied NLP and sentiment analysis on customer feedback data to identify operational gaps and recommend vendor and product optimization strategies. • Designed A/B testing frameworks and campaign measurement strategies, improving campaign effectiveness and increasing conversion performance across multiple marketing initiatives. • Implemented market basket analysis and pricing analytics models to ide

Data Scientist

TATA MOTORS

Jan 2023 - Jul 2023 (7 months)

• Developed predictive maintenance models using IoT sensor time-series data to forecast equipment failures and improve production reliability across manufacturing lines. • Built PySpark ETL pipelines integrating machine telemetry and operational datasets to support real-time monitoring and predictive analytics initiatives. • Applied anomaly detection and statistical modeling to identify early failure signals across production systems and reduce unexpected equipment breakdowns. • Engineered time-series features and model pipelines improving predictive accuracy and enabling proactive maintenance planning across multiple plants. • Created dashboards and monitoring tools for plant managers, improving visibility into machine performance and main

Data Scientist

Nykaa

Jun 2021 - Dec 2022 (1 year 7 months)

• Built hybrid recommendation systems combining collaborative filtering and content-based approaches to improve product personalization across web and mobile platforms. • Developed feature pipelines using transactional, clickstream, and product metadata to enhance recommendation quality and capture customer intent. • Implemented daily retraining pipelines using Spark and cloud infrastructure to maintain recommendation accuracy for large-scale user traffic. • Conducted A/B testing and experimentation to evaluate recommendation algorithms, improving click-through rate and conversion performance. • Developed cold-start solutions using embeddings and session-based modeling to support new users and product launches. • Built dashboards tracking e

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