Solving AI & Data Problems.
Proven Value and Impact.
I'm Achal Dixit! an AI Products & Strategy Lead and a Lead Data Scientist with a track record across pharma, genetics, logistics, and life sciences. I design, build, and ship systems that work in production from clinical trial automation to supply chain intelligence. I come in when organisations need Senior/Fractional AI & Data expertise to set strategy, architect production systems, and deliver outcomes whether prototypes or production.
$4.5M
Vendor cost eliminated via GenAI
$15M+
Savings from trial optimization
90%+
Precision on RTO forecasting
60%
Audit prep time reduction
Podcast
On air with Josh Starmer
StatQuest has over a million subscribers. This conversation on ML, healthcare AI, and getting research into production has driven more inbound than anything else I've published.
Education
2026 — 2027
The University of Texas at Austin
MS, Data Science & Artificial Intelligence
Concurrent with industry roles at Sanofi and BMS.
2020 — 2022
Imperial College London
Visiting Research Scholar · Department of Life Sciences
Genomics intelligence for Target Malaria. Genetic origin classification of malaria vectors using SNPs and interpretable ML in Dr. Burt's Lab.
2018 — 2022
IIIT Guwahati
B.Tech, Computer Science & Engineering · 8.08 GPA
Specialised in ML, optimisation, and secure systems. Research accolades in AI for healthcare.
Services
What I build with you
Modular engagements — strategy sprint to embedded fractional leadership. Every offering has a clear deliverable.
01
AI Strategy & Roadmaps
Enterprise AI strategies, governance, and measurable value streams. Fractional CTO/CDO capacity without the overhead.
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02
GenAI & Agentic Systems
LLM-native applications with proper RAG architecture, audit trails, and production observability baked in from day one.
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03
ML Engineering
Feature engineering to CI/CD — production ML with reproducibility, monitoring, and compliance throughout.
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04
Regulatory & Clinical AI
Validated analytics for FDA-readiness, pharmacovigilance, and trial optimization — with explainability that survives audit.
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05
Operations & Supply Chain AI
Predictive intelligence and variance diagnostics for high-velocity operations — demand sensing to cost decomposition.
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06
Drug Intelligence & Genomics
Patient embeddings, drug repurposing pipelines, and genomics ML for early-stage biotech and academic research.
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Selected Work
Outcomes that shipped
Representative engagements across pharma, logistics, and life sciences.
Inspection Readiness Intelligence
Bristol Myers Squibb
Automated ingestion of FDA 483s, internal audits, and SOP deviations to surface systemic risk and remediation roadmaps for global supply leadership. Presented at BioIT World, Boston 2025.
60% audit prep time reduction · weeks → minutes · Science & Innovation Award 2025
Automated TFL Generation for Clinical Study Reports
Bristol Myers Squibb
State-of-the-art automated Tables, Figures & Listings using GenAI with a clean analytics layer supporting medical writers across biologics and small molecule programs.
$4.5M external vendor eliminated · medical writer productivity 2×
Trial Simulation & Design Optimization
Bristol Myers Squibb
End-to-end trial simulation modelling cost, risk, and timelines. Rebuilt the Study Trial Optimization tool — study ingestion from 7–8 days to hours, with half the resources.
$2–15M savings per trial · $1.2M annual ops savings
Nationwide Return-to-Origin Forecasting
Delhivery
Production RTO model for ZARA, H&M, and Urbanic — beating industry SOTA by weaving geospatial signals, intent data, and operational levers. Deployed as a revenue-generating value-added service.
90%+ precision & recall
Variance Diagnostics Copilot
Delhivery
AI that narrates why KPIs move — fusing causal inference, temporal clustering, and natural language insights for operators across 1,000+ last-mile centers.
Reporting: one week → minutes · 7–10% daily closure improvement
Drug Repurposing Patient Embeddings
ChAIron
Patient embeddings from 2M+ patient datasets spanning ICD, ATC, and genomics codes. Automated interpretability, dashboards, and drug repurposing pipeline engineering for a Swiss pre-seed biotech.
$25K+ in cloud compute savings · active ongoing engagement
Malaria Vector Genomics ML
Imperial College London
Reproducible ML workflows analysing 4.8M genomic features for Target Malaria — automated QC, LD pruning, and rare allele detection.
Published · targetmalaria.org
Experience
Where I've delivered
2026 — Now
Sanofi
AI Products & Strategy Lead
Leading AI product strategy at one of the world's largest pharmaceutical organisations — defining roadmaps, shaping governance, and driving enterprise-scale adoption.
2024 — 2026
Bristol Myers Squibb
Lead Data Scientist
Led AI across inspection readiness, regulatory knowledge discovery, clinical trial optimization, and global manufacturing intelligence. Science & Innovation Award, 2025.
2024 — Now
ChAIron
Founding Head of AI & Data(Fractional)
Swiss pre-seed biotech. Patient embeddings from 2M+ records, interpretability automation, and drug repurposing pipelines. $25K+ in cloud compute savings delivered.
2022 — 2024
Delhivery
Lead, Decision Intelligence
Architected India's largest logistics platform's ML foundation — RTO, FDDS, and closure models deployed nationwide.
2022
ZS Associates
Business Technology Solutions Analyst
Big Data Analytics for Pfizer and AbbVie. Built HCP360 data lake and analytics pipelines across on-premises clusters, cloud, and commercial data lakes.
2020 — 2021
Imperial College London
Visiting Research Scholar
Genomics intelligence for Target Malaria — genetic origin classification using SNPs and interpretable ML, Dr. Burt's Lab, Life Sciences.
Recognition
Science & Innovation Award
BMS Hyderabad · 2025
BioIT World Keynote Speaker
Boston, MA · 2025
MIT Covid-19 Challenge — Winner
5,000+ global participants
MHRD Samadhan Challenge — Winner
800+ teams · Govt. of India
Microsoft Imagine Cup — India Finalist
Top 5 of 150+ teams
Research
Know Your Audits: Using LLMs to Mitigate Risk and Enhance Pharma Supply Chain and Manufacturing Resilience
BioIT World Conference, Boston, MA · Keynote · 2025
Predicting severe outcomes in Covid-19 related illness using only patient demographics, comorbidities and symptoms
The American Journal of Emergency Medicine, Vol. 45 · 2021
Demystifying Heart Failure with Mid-Range Ejection Fraction Using Machine Learning
48th Computing in Cardiology (CinC 2021)
A Machine Learning Approach to Detect Congestive Heart Failure Without Ejection Fraction
ICICA 2021 · Abstract Presentation
Let's build
something real.
Available for fractional engagements, advisory, and speaking. I work with a small number of clients at a time.
Consulting & projectsachaldixit22@gmail.com
Research partnershipsachal.dixit@iiitg.ac.in