Marketing Plan
Vishal Sood — Senior Research Engineer · February 2026
1. Positioning Statement
Senior Research Engineer (PhD, Statistical Physics) with 15+ years building high-performance computational systems for scientific discovery. Deep expertise in designing parallel workflows for multi-terabyte datasets, reproducible scientific pipelines, and data-driven validation frameworks across neuroscience, genomics, and geospatial data.Main experience: Architecting production-grade scientific computing platforms — from HPC cluster orchestration (SLURM, 100+ nodes) to clinical-grade genomics pipelines (C++/Python) to interactive data analysis tools serving front-line researchers.
Area of expertise: The intersection of first-principles algorithmic thinking and software engineering. Translating complex research requirements into scalable, maintainable systems that accelerate discovery. Proven ability to adapt rapidly across scientific domains — each career transition driven by the transferability of computational methods.
Personal qualities: Intellectually curious, rigorous, collaborative. Thrives in small teams where technical depth is valued. Communicates complex ideas clearly across disciplines. Committed to reproducibility, open science, and the thesis-proof structure: every capability claim backed by evidence.
2. Areas of Competency
Scientific Computing & HPC Systems- Massively parallel workflows for multi-terabyte datasets (SLURM, Spark, Dask)
- C++ performance optimization for 100+ node clusters
- High-throughput pipelines with HDF5, Parquet, lazy-loading APIs
- Configuration-driven pipeline architectures for reproducible computing
Algorithm Development & Statistical Modeling
- Novel algorithms for pattern detection in high-dimensional noisy data
- Monte Carlo simulation, stochastic models, Bayesian methods
- Machine learning pipelines for scientific classification
- Network analysis, graph theory, computational geometry
Data Engineering & Knowledge Management
- FAIR data principles, metadata schemas, knowledge graphs
- Scientific data formats: HDF5, NRRD, Parquet, NetCDF, VCF, BAM
- REST API design for distributed data services
- Multi-database integration frameworks
Bioinformatics & Genomics Pipelines
- Genomic variant annotation and ACMG-based classification systems
- High-performance C++ backends for clinical-grade genomic analysis
- Integration of ClinVar, gnomAD, and proprietary databases
- Tools: Samtools, GATK, freebayes, Bioconductor
Scientific Workflow Development & Automation
- Reproducible, parameterized computational pipelines
- Automated report generation ("scientific narrative" engines)
- CI/CD for scientific software, containerized applications
- Batch management and intelligent job scheduling for HPC
Software Architecture & API Design
- Plugin-based extensible frameworks (Adapter Pattern)
- Advanced Python metaprogramming (metaclasses, descriptors)
- Declarative, self-documenting APIs for scientific tools
- Modern C++ (C++11/14/17) with functional programming paradigms
3. Target Market
Geographic area: Arc Lemanique (Lausanne, Geneva), extending to Bern, Zurich, Basel. Open to remote/hybrid arrangements.Target industries:
- AI / Machine Learning platforms and infrastructure
- Life Sciences / Biotech / Computational Biology
- Scientific Computing and Research Software
- Quantitative Finance / Financial Technology
- Climate Technology and Earth Sciences
Company size: 10-500 employees preferred (startup to mid-size), also research divisions within larger organizations (EPFL, CERN, ETH, Roche).
Desired culture: Technical depth valued over process. Collaborative, research-friendly environment. Modern development practices. Small teams with direct impact.
Target roles:
- Senior Research Engineer / Scientific Software Developer
- Senior Quantitative Research Engineer
- Computational Biology Specialist / Bioinformatics Engineer
- Research Software Engineer / ML Infrastructure Engineer
Salary range: 140,000-170,000 CHF (flexible depending on role and company stage)
4. Target Companies
AI & Machine Learning *(Priority: OMG!!)*- Anthropic — frontier AI research (Research Engineer)
- Meta — AI infrastructure
- Lakera — AI security (Lausanne)
- Daedalean — autonomous flight AI (Zurich)
- Visium — applied AI consulting (Lausanne)
Life Sciences & MedTech *(Priority: OMG!!)*
- Isomorphic Labs — AI for drug discovery
- Hedera-Dx — cancer diagnostics, cfDNA
- Maxwell Biosystems — neural interfaces, MEA platforms
- Adaptyv Bio — protein engineering
- Alithea Genomics — RNA sequencing
- NVIDIA — computational biology / Clara platform
- Hilo by Aktiia — health monitoring
Quantitative Finance *(Priority: SUPER!)*
- IMC Trading — Python infrastructure, digital assets (Zug)
- SwissQuant — quantitative risk analytics
- Evooq — wealth management technology
- Keyrock — algorithmic trading
- PartnerRe — reinsurance analytics
Academia & Research *(Priority: SUPER!)*
- EPFL — scientific computing, Blue Brain legacy
- CERN — data engineering, physics computing
- ETH Zurich — computational science
- FMI Basel — computational biology
- University of Bern — research engineering
Climate & Science Tech *(Priority: WHY NOT!)*
- Jua — AI weather prediction
- TetraScience — scientific data cloud
Hardware & Quantum *(Priority: WHY NOT!)*
- Corintis — semiconductor technology
- Zurich Instruments — quantum computing control
- ANYbotics — autonomous robotics
Software Development *(Priority: WHY NOT!)*
- SonarSource — code quality (Geneva)
- Bending Spoons — consumer apps
- Thomson Reuters — information services
Startups to Watch (2026)
- Cradle — AI protein design
- Neural Concept — AI for engineering simulation
- Synthara AG — neuromorphic computing
- Cerrion, EthonAI — industrial AI
- Bloom, DeepJudge, Mentiora AI — AI-native software
5. Action Plan
Weekly targets:- 2-3 tailored applications per week
- 1 networking conversation (coffee, LinkedIn, meetup)
- 1 technical contribution (open source, blog post, or portfolio improvement)
Active channels:
- Direct applications via company career pages
- Recruitment agencies: ComputerFutures, SwissPeak-Partners
- LinkedIn networking and content
- Personal website with AI-powered portfolio
Upskilling (ongoing):
- RAG systems and applied AI engineering
- Cloud infrastructure (AWS, Docker, Kubernetes)
- Modern ML frameworks (PyTorch, JAX)
Networking strategy:
- EPFL / ETH alumni events
- Swiss AI and data science meetups
- Direct outreach to hiring managers via LinkedIn
Recruitment agency engagement:
- ComputerFutures — specialist tech staffing
- SwissPeak-Partners — senior tech placements
- Proactive sharing of tailored resume variants per opportunity