Nishant Agrawal

CEO

Delhi, India10 yrs 2 mos experience

Most Likely To SwitchHighly Stable

Key Highlights

10+ years in Data Science and Machine Learning.
Led high-scale ML engineering teams at Swiggy.
Achieved significant improvements in prediction accuracy.

Stackforce AI infers this person is a Data Science leader in B2C marketplaces with a focus on machine learning and optimization.

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Skills

Core Skills

Machine LearningData Analytics

Other Skills

Deep LearningOptimizationAlgorithmsBandit AlgorithmsA/B TestingBacktestingCustomer SegmentationCC++JavaRSASPythonMatlabLinux

About

Senior Data Science and Machine Learning leader with 10+ years of experience building prediction and optimisation systems that directly influence efficiency, conversion, and cost outcomes in large consumer marketplaces. Currently leading ML engineering teams at Swiggy, owning high-scale, low-latency real-time decision systems in a large consumer marketplace. Strong background in experimentation, optimisation, and ML systems delivery, with a consistent focus on turning data science into scalable business impact.

Experience

10 yrs 2 mos

Total Experience

2 yrs 6 mos

Average Tenure

5 yrs 2 mos

Current Experience

Swiggy

3 roles

Senior Manager - Data Science

Promoted

Oct 2023 – Present · 2 yrs 7 mos

Leadership & Ownership
Leading a 10-member Data Science & ML team in Marketplace Logistics Optimisation, owning roadmap, delivery, and quality of production ML systems.
Partnered with senior cross-functional stakeholders (Product, Engineering, Operations, including SVP-level leadership) to align ML roadmaps with business priorities.
Drove adoption of GenAI-assisted workflows (code review, analysis acceleration, documentation), improving team productivity and code quality standards.
Customer & Marketplace Prediction Systems
Led development of customer-facing ETA prediction systems using deep learning, achieving ~10% accuracy improvement and ~20% uplift in downstream customer metrics.
Improved ETA consistency across the user journey by ~20% through a two-stage modelling framework, reducing customer cancellations.
Improved restaurant preparation time prediction accuracy by ~10% using dish embeddings and bandit-based learning, improving matching efficiency and restaurant experience.
Dispatch, Supply & Optimisation Systems
Built driver time utilisation prediction models across restaurant–customer pairs, improving serviceability decisions and overall system efficiency.
Led the evolution of next-generation dispatch algorithms via objective function formulation to improve customer & driver experience, pooling decisions, and cost efficiency.
Built near–real-time driver incentive optimisation systems using bandit algorithms (UCB), achieving 5% incentive cost reduction while maintaining supply–demand balance.
Created driver mobilisation and geo-positioning systems improving ~5% driver availability in supply-deficit geographies.

Machine LearningData AnalyticsDeep LearningOptimizationAlgorithms

Senior Data Scientist

Promoted

Apr 2022 – Sep 2023 · 1 yr 5 mos

Data Scientist

Jan 2021 – Mar 2022 · 1 yr 2 mos

Bounce

Senior Data Scientist

Nov 2019 – Dec 2020 · 1 yr 1 mo · Bengaluru, Karnataka, India

Built a bike reliability prediction system reducing bad ride experiences by 20%.
Deployed real-time ML scoring and A/B experimentation systems with alerting and fallbacks.
Built simulation frameworks for demand–supply, geo transitions, and asset damage risk.
Mentored junior team members across ML, Python/SQL, and stakeholder communication.

Franklin templeton investments

Data Scientist

Oct 2017 – Oct 2019 · 2 yrs

Built ML-based quantitative models achieving ~3× risk-adjusted returns vs benchmarks, supported by rigorous backtesting.
Built macroeconomic forecasting pipelines with automated time-series training.
Developed loan prepayment prediction models outperforming benchmarks by ~6%.

Operasolutions

2 roles

Senior Analytics Specialist

Promoted

Jul 2017 – Sep 2017 · 2 mos · Noida Area, India

Built spend attrition prediction models using deep learning on transaction data.
Led customer segmentation using feature engineering, PCA, and clustering.