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associate scientist

computational materials discovery scientist

Scouto AI

<div>
 Position Summary
 
 
 As a Computational Materials Discovery Scientist, you will work at the intersection of materials science, computational chemistry, condensed matter physics, quantum computing, and AI/ML. You will contribute to first-principles simulations, molecular and mesoscale modeling, materials informatics pipelines, and hybrid quantum&ndash;classical algorithms for accelerated discovery of materials, catalysts, semiconductors, polymers, and functional systems.
 
 
 This role is ideal for candidates who want to solve real scientific and industrial problems using DFT, MD, multiscale modeling, machine learning, and emerging quantum computing techniques, fully integrated with QpiAI&rsquo;s AI and quantum computing platforms.
 
 
 
 Requirements
 
 
 
 Core Technical Skills
 
 
 <ul>
 <li>
 Computational &amp; Quantum Mechanical Methods
 
 
 </li>
 </ul>
 Electronic Structure &amp; Quantum Methods
 
 
 <ul>
 <li>
 Density Functional Theory (DFT)
 </li>
 <li>
 Ab initio Molecular Dynamics (AIMD)
 </li>
 <li>
 Time-dependent DFT (TDDFT) for excited states
 </li>
 <li>
 DFT+U for strongly correlated systems
 </li>
 <li>
 Hybrid functionals
 </li>
 <li>
 Hartree&ndash;Fock and post-HF methods (MP2, CCSD(T))
 
 
 </li>
 </ul>
 Molecular &amp; Statistical Simulations
 
 
 <ul>
 <li>
 Classical Molecular Dynamics (MD)
 </li>
 <li>
 Force-field development and validation
 </li>
 <li>
 Monte Carlo (MC) simulations
 </li>
 <li>
 Kinetic Monte Carlo (kMC) for surface reactions
 </li>
 <li>
 Machine Learning Force Fields ( Fairchem, Universal Forcefield, MACE)
 
 
 </li>
 </ul>
 Multiscale &amp; Mesoscale Modeling
 
 
 <ul>
 <li>
 Hierarchical nano &rarr; micro &rarr; meso &rarr; macro modeling
 </li>
 <li>
 QM&ndash;MM and QM&ndash;continuum coupling
 </li>
 <li>
 Microkinetic modeling and reaction networks
 </li>
 <li>
 Phase-field modeling
 </li>
 <li>
 Free energy methods (umbrella sampling, metadynamics)
 </li>
 <li>
 Materials Simulation &amp; Electronic Structure Modeling
 </li>
 <li>
 Bulk and surface DFT calculations
 </li>
 <li>
 k-point convergence, basis set testing
 </li>
 <li>
 Band structure, DOS, PDOS
 </li>
 <li>
 Phonons and lattice dynamics
 </li>
 <li>
 Elastic constants, thermal &amp; mechanical properties
 </li>
 <li>
 Defect formation energies
 </li>
 <li>
 Phase stability &amp; convex hulls
 </li>
 <li>
 Slab models and adsorption energies
 </li>
 <li>
 Reaction pathways using NEB / CI-NEB
 
 
 </li>
 </ul>
 Benchmarking &amp; Validation
 
 
 <li>
 Cross-code benchmarking across:
 </li>
 <ul>
 <li>
 DFT engines: VASP, Quantum ESPRESSO, CP2K, GPAW, CASTEP, Gaussian, ORCA
 </li>
 <li>
 MD engines: LAMMPS, GROMACS, NAMD
 </li>
 <li>
 ML potential
 </li>
 <li>
 Materials Informatics &amp; AI/ML
 </li>
 <li>
 Build ML models for materials &amp; catalyst property prediction:
 </li>
 <ul>
 <li>
 Fairchem, Universal Forcefield, CGCNN, MEGNet, SchNet, e3nn, NequIP
 </li>
 <li>
 Transformer &amp; foundation models for materials
 </li>
 </ul>
 <li>
 Curate datasets from:
 </li>
 <ul>
 <li>
 Materials Project, OQMD, NOMAD, JARVIS
 </li>
 </ul>
 <li>
 Develop ML surrogates for:
 </li>
 <ul>
 <li>
 Energy &amp; force prediction
 </li>
 <li>
 Bandgap estimation
 </li>
 <li>
 Thermal &amp; mechanical properties
 </li>
 <li>
 Catalyst screening &amp; ranking
 </li>
 </ul>
 <li>
 Integrate ML pipelines with:
 </li>
 <ul>
 <li>
 DFT / MD workflows
 </li>
 <li>
 Quantum simulation pipelines
 </li>
 </ul>
 <li>
 Simulation Workflow Engineering
 </li>
 <li>
 Build reproducible, automated workflows in Python for:
 </li>
 <ul>
 <li>
 High-throughput materials screening
 </li>
 <li>
 DFT&ndash;MD&ndash;CG-Mesoscale simulation pipelines
 </li>
 <li>
 Data extraction &amp; post-processing
 </li>
 </ul>
 <li>
 Develop modular tools for:
 </li>
 <ul>
 <li>
 Structure parsing (CIF, POSCAR, XYZ, PDB)
 </li>
 <li>
 Geometry builders &amp; surface generators
 </li>
 <li>
 Parameters generations
 </li>
 <li>
 Visualization (band structure, DOS, phonons, trajectories)
 </li>
 </ul>
 <li>
 Deploy workflows on:
 </li>
 <ul>
 <li>
 HPC clusters
 </li>
 <li>
 Cloud platforms (AWS, GCP)
 </li>
 <li>
 Containerized environments (Docker)
 </li>
 </ul>
 <li>
 Research, Collaboration &amp; Documentation
 </li>
 <li>
 Conduct literature reviews in:
 </li>
 <ul>
 <li>
 Computational materials
 </li>
 <li>
 Catalysis
 </li>
 <li>
 Semiconductors
 </li>
 <li>
 Alloy and Ceramics
 </li>
 <li>
 Polymers
 </li>
 <li>
 Quantum algorithms
 </li>
 </ul>
 <li>
 Design, execute, and analyze numerical experiments
 </li>
 <li>
 Prepare:
 </li>
 <ul>
 <li>
 Technical reports
 </li>
 <li>
 Internal whitepapers
 </li>
 <li>
 Presentations and datasets
 </li>
 </ul>
 <li>
 Collaborate closely with:
 </li>
 <ul>
 <li>
 Quantum hardware teams
 </li>
 <li>
 Algorithm developers
 </li>
 <li>
 AI/ML engineers
 
 </li>
 </ul>
 </ul>
 Specialization Tracks
 
 
 <ul>
 <li>
 DFT &amp; Electronic Structure Specialization
 </li>
 <li>
 Advanced XC functional selection &amp; benchmarking
 </li>
 <li>
 Strongly correlated systems (DFT+U, Hubbard models)
 </li>
 <li>
 Excited-state calculations (TDDFT, GW &ndash; exposure preferred)
 </li>
 <li>
 Defects, surfaces, and interfaces
 </li>
 <li>
 Electronic transport &amp; conductivity modeling
 </li>
 <li>
 Molecular Dynamics &amp; Classical Simulations
 </li>
 <li>
 Classical MD simulations (LAMMPS, GROMACS)
 </li>
 <li>
 Force-field parameterization &amp; validation
 </li>
 <li>
 Free energy calculations
 </li>
 <li>
 Reactive force fields (ReaxFF)
 </li>
 <li>
 ML-accelerated MD workflows
 </li>
 <li>
 Parameter generation for coarse-grained simulations
 </li>
 <li>
 Catalysis Specialization
 </li>
 <li>
 Heterogeneous, homogeneous &amp; electro-catalysis
 </li>
 <li>
 Reaction pathway identification
 </li>
 <li>
 Transition state searches (NEB, CI-NEB)
 </li>
 <li>
 Adsorption energies &amp; surface thermodynamics
 </li>
 <li>
 Microkinetic modeling
 </li>
 <li>
 Applications:
 </li>
 <ul>
 <li>
 OER, ORR, HER
 </li>
 <li>
 Photocatalysis
 </li>
 <li>
 Single-atom &amp; nanocluster catalysts
 </li>
 </ul>
 <li>
 Polymers &amp; Soft Matter Specialization
 </li>
 <li>
 DFT-based parameter extraction for polymers
 </li>
 <li>
 Multiscale polymer modeling (DFT, AA, CG)
 </li>
 <li>
 Dissipative Particle Dynamics (DPD)
 </li>
 <li>
 Monte Carlo Simulations
 </li>
 <li>
 Polymer blends, Polymer nanocomposites, surfactants, colloids
 </li>
 <li>
 Polymerization, degradation, crosslinking, morphology and aging studies
 </li>
 <li>
 Integration of DFT &rarr; MD &rarr; DPD&rarr;Phase field simulations pipelines
 </li>
 <li>
 Quantum Computing for Materials Simulation
 </li>
 <li>
 Map materials Hamiltonians to qubits:
 </li>
 <ul>
 <li>
 Jordan&ndash;Wigner, Bravyi&ndash;Kitaev, parity mappings
 </li>
 </ul>
 <li>
 Work on quantum algorithms including:
 </li>
 <ul>
 <li>
 VQE for correlated materials
 </li>
 <li>
 Subspace Quantum Diagonalization (SQD)
 </li>
 <li>
 qEOM for excited states
 </li>
 <li>
 Quantum Phase Estimation
 </li>
 <li>
 QITE / Quantum Monte Carlo
 </li>
 </ul>
 <li>
 Analyze:
 </li>
 <ul>
 <li>
 Qubit requirements
 </li>
 <li>
 Circuit depth
 </li>
 <li>
 Noise &amp; error budgets
 </li>
 </ul>
 <li>
 Design material-specific ans&auml;tze for NISQ devices and simulators
 
 </li>
 </ul>
 Software &amp; Programming Skills
 
 
 <ul>
 <li>
 DFT Codes: VASP, QE, CP2K, Gaussian, ORCA, CASTEP, ADF
 </li>
 <li>
 MD Codes: LAMMPS, GROMACS, NAMD, AMBER
 </li>
 <li>
 Visualization: VMD, VESTA, OVITO, Materials Studio, ASE
 </li>
 <li>
 Programming: Python (mandatory), Bash
 </li>
 <li>
 ML: PyTorch, TensorFlow, scikit-learn
 </li>
 <li>
 Infrastructure: HPC, MPI, Docker, Git, AWS / GCP
 
 
 </li>
 </ul>
 Soft Skills
 
 
 <ul>
 <li>
 Strong analytical and first-principles thinking
 </li>
 <li>
 Ability to design reproducible scientific workflows
 </li>
 <li>
 Clear scientific communication
 </li>
 <li>
 High ownership and curiosity-driven research mindset
 
 
 </li>
 </ul>
 
 Educational Qualifications
 
 
 
 <ul>
 <li>
 PhD (or pursuing PhD for intern role) in:
 </li>
 <ul>
 <li>
 Chemistry
 </li>
 <li>
 Materials Science
 </li>
 <li>
 Chemical Engineering
 </li>
 <li>
 Physics
 </li>
 <li>
 Computational Science or related STEM field
 </li>
 </ul>
 <li>
 Strong foundation in:
 </li>
 <ul>
 <li>
 Physical chemistry
 </li>
 <li>
 Quantum mechanics
 </li>
 <li>
 Statistical mechanics &amp; thermodynamics
 </li>
 </ul>
 <li>
 Specialization in computational chemistry / materials modeling strongly preferred
 
 </li>
 </ul>
 
 Preferred Qualifications
 
 
 
 <ul>
 <li>
 Publications or strong computational project portfolio
 </li>
 <li>
 Experience with HPC &amp; large-scale simulations
 </li>
 <li>
 Prior work in:
 </li>
 <ul>
 <li>
 Materials discovery
 </li>
 <li>
 Catalysis
 </li>
 <li>
 Semiconductor
 </li>
 <li>
 Polymer modeling
 </li>
 <li>
 ML-driven materials science
 </li>
 </ul>
 <li>
 Exposure to quantum algorithms or hybrid quantum&ndash;classical workflows
 
 </li>
 </ul>
 Skills: ml,dft,materials,simulations,modeling
</div>

Computational Materials Discovery Scientist

QpiVolta Technologies

<div>
 
 
 Sodium-Ion Battery
 
 
 
 
 
 
 
 About the Role
 
 
 
 
 
 
 We are looking for an experienced
 
 Battery Scientist-Sodium-Ion
 
 to join our advanced R&amp;D and product development team. The candidate will contribute to the design, scale-up, and integration of advanced battery technologies with a strong focus on translating lab-scale innovation into manufacturable solutions. This position requires a balance of hands-on experimentation, problem-solving, and collaboration with engineering and manufacturing teams.
 
 
 
 
 
 
 Key Responsibilities
 
 
 
 
 
 <ul>
 <li>
 Develop and optimize Sodium-Ion electrodes and electrolytes for scalable manufacturing.
 </li>
 <li>
 Lead process development for slurry preparation, coating, densification, and cell assembly with a focus on reproducibility and cost-effectiveness.
 </li>
 <li>
 Conduct electrochemical performance evaluation (cycling, rate capability, safety, interfacial stability) to benchmark against industrial standards.
 </li>
 <li>
 Work closely with process engineers to transition materials and processes from lab to pilot-scale production.
 </li>
 <li>
 Support intellectual property (IP) generation through patents and technical documentation.
 </li>
 <li>
 Provide technical input for collaborations with suppliers, customers, and external partners.
 </li>
 <li>
 Ensure compliance with safety, quality, and environmental standards in all lab and pilot-scale activities.
 </li>
 </ul>
 
 
 
 
 
 Qualifications
 
 
 
 
 
 <ul>
 <li>
 PhD in Electrochemistry, Materials Science, Chemical Engineering, or related discipline with good publications and or patents records.
 </li>
 <li>
 Proven experience in
 
 Sodium-ion chemistry development
 
 with demonstrated contributions in material optimization, characterizations, and cell integration.
 </li>
 <li>
 Hands-on expertise in
 
 electrode&ndash;electrolyte interfaces
 
 , cell assembly (coin/pouch), and electrochemical testing.
 </li>
 <li>
 Strong background in
 
 process development and scale-up
 
 in battery industry.
 </li>
 <li>
 Excellent data analysis, technical reporting, and cross-functional collaboration skills.
 </li>
 </ul>
 
 
 
 
 
 Preferred Qualifications
 
 
 
 
 
 <ul>
 <li>
 Industry experience in a battery manufacturing or energy storage company.
 </li>
 <li>
 Familiarity with
 
 pilot-line operations, coating technologies, and dry/wet processing, synthesis techniques
 
 .
 </li>
 <li>
 Knowledge of
 
 supply chain requirements
 
 for advanced battery materials.
 </li>
 <li>
 Experience contributing to IP, tech transfer, or commercialization projects.
 </li>
 </ul>
 
 
 
 
 
 What We Offer
 
 
 
 
 
 <ul>
 <li>
 Opportunity to work on cutting-edge
 
 Sodium-Ion battery commercialization
 
 .
 </li>
 <li>
 Exposure to
 
 end-to-end product development
 
 from materials discovery to pilot-scale production.
 </li>
 <li>
 Competitive salary and benefits package.
 </li>
 <li>
 Dynamic and collaborative work environment with clear career progression.
 </li>
 </ul>
 
 
</div>

Research Scientist

Syngene International Limited

<div>
 Date: 10 Oct 2025
 
 
 Location:
 
 
 Bangalore, KA, IN, 560099
 
 
 Division: Discovery Services
 
 
 
 Job Title: Invivo Pharmacology Scientist
 
 
 
 
 Job Location: Bangalore
 
 
 
 
 JOB PURPOSE (Briefly Describe The Objectives Of The Job)
 
 
 
 Responsible for executing the assigned in vivo efficacy studies in metabolic disorder therapeutic area.
 
 
 
 Key Responsibilities
 
 
 
 <ul>
 <li>
 Responsible to conduct assigned research projects including experiments with utilizing current concepts and recognized standard techniques.
 </li>
 <li>
 Responsible to set up the experimental designs, analysis of data and concluding from the progress.
 </li>
 <li>
 Record laboratory observations information in lab notebooks, database and document the experimental designs.
 </li>
 <li>
 Responsible for data analysis, perform calculations for the result of the experiment.
 </li>
 <li>
 Summarize the research and communicate result of research to the supervisor.
 
 
 </li>
 </ul>
 
 Educational Qualification
 
 
 
 <ul>
 <li>
 M.Pharm Pharmacology
 </li>
 <li>
 M.V.Sc Pharmacology / Pathology
 </li>
 <li>
 M.Sc Neuroscience / Biochemistry / Biotechnology
 
 
 </li>
 </ul>
 
 Work Experience
 
 
 
 0 - 6 years of Industrial experience
 
 
 
 Technical /Functional Skills
 
 
 
 <ul>
 <li>
 Hands-on experience in execution of CNS disease models which includes psychiatric disorders such as anxiety, depression, PTSD etc., and neurological conditions like Alzheimer&rsquo;s disease, Parkinson&rsquo;s disease, cerebral ischemia and epilepsy.
 </li>
 <li>
 Hands-on experience in animal behavioral observation, quantification of animal behavior, experience in stereotaxic surgery, microdialysis, cannulation techniques, immunohistochemistry and ELISA.
 </li>
 <li>
 Will work independently or in association with senior scientists and other associates in the design and execution of in-vivo efficacy experiments in the relevant therapeutic area.
 </li>
 <li>
 Experience in animals handling (rodents and non rodents) such as dosing by various routes, bleeding by various routes, injections by various routes and surgical modalities which includes cannulation etc&hellip;
 </li>
 <li>
 Associate to provide technical, analysis and reporting outcomes of research for projects in relevant therapeutic area.
 </li>
 <li>
 Organizational skills and the ability to work in a highly collaborative environment are required.
 
 
 </li>
 </ul>
 
 Behavioral/Managerial Skills
 
 
 
 <ul>
 <li>
 Good communication skills
 </li>
 <li>
 Excellent interpersonal skills.
 </li>
 <li>
 Good team player
 </li>
 <li>
 Strong documentation Skills.
 
 
 </li>
 </ul>
 
 Equal Opportunity Employer
 
 
 
 It is the policy of Syngene to provide equal employment opportunity (EEO) to all persons regardless of age, color, national origin, citizenship status, physical or mental disability, race, religion, creed, gender, sex, sexual orientation, gender identity and/or expression, genetic information, marital status, status with regard to public assistance, veteran status, or any other characteristic protected by applicable legislation or local law. In addition, Syngene will provide reasonable accommodation for qualified individuals with disabilities.
</div>

in vivo pharmacologist

<div>
 Date: 2 Dec 2025
 
 
 Location:
 
 
 Bangalore, KA, IN, 560099
 
 
 Division: Discovery Services
 
 
 
 Job Title: In Vivo PK scientist
 
 
 
 
 Job Location: Bengaluru
 
 
 
 About Syngene : Syngene (www.syngeneintl.com) is an innovation-led contract research, development and manufacturing organization offering integrated scientific services from early discovery to commercial supply.
 
 
 
 At Syngene, safety is at the heart of everything we do personally and professionally. Syngene has placed safety at par with business performance with shared responsibility and accountability, including following safety guidelines, procedures and SOPs, in letter and spirit
 
 
 
 
 Mandatory expectation for all roles as per Syngene safety guidelines
 
 
 
 <ul>
 <li>
 Overall adherence to safe practices and procedures of oneself and the teams aligned
 </li>
 <li>
 Contributing to development of procedures, practices and systems that ensures safe operations and compliance to company&rsquo;s integrity &amp; quality standards
 </li>
 <li>
 Driving a corporate culture that promotes environment, health, and safety (EHS) mindset and operational discipline at the workplace at all times.
 </li>
 <li>
 Ensuring safety of self, teams and lab/plant by adhering to safety protocols and following environment, health, and safety (EHS) requirements at all times in the workplace.
 </li>
 <li>
 Ensure all assigned mandatory trainings related to data integrity, health, and safety measures are completed on time by all members of the team including self
 </li>
 <li>
 Compliance to Syngene&rsquo; s quality standards at all times
 </li>
 <li>
 Hold self and their teams accountable for the achievement of safety goals
 </li>
 <li>
 Govern and Review safety metrics from time to time
 
 
 </li>
 </ul>
 
 Core Purpose of the Role:
 
 Conduct of
 
 in vivo
 
 pharmacokinetics study in rodents
 
 
 
 Education:
 
 M. Pharm /PHD (Pharmacology &amp; Toxicology) with an expert in conducting the studies in an industry setting
 
 
 
 Role Accountabilities
 
 
 
 <ul>
 <li>
 Conduct of pharmacokinetic studies in rodents.
 </li>
 <li>
 Documenting the lab notebooks online and compound management.
 </li>
 <li>
 Ability to adapt to the dynamic team environment.
 </li>
 <li>
 Possess the knowledge and exposure to environment, health, and safety (EHS) practices
 </li>
 <li>
 Follow environment, health, and safety (EHS) requirements at all times in the workplace ensuring individual and lab/plant safety
 </li>
 <li>
 Ensure environment, health, and safety (EHS) measures are imparted via training and adhered within the team.
 
 
 </li>
 </ul>
 
 Syngene Values
 
 
 
 All employees will consistently demonstrate alignment with our core values
 
 
 <ul>
 <li>
 Excellence
 </li>
 <li>
 Integrity
 </li>
 <li>
 Professionalism
 
 
 </li>
 </ul>
 
 Skills And Capabilities
 
 
 
 <ul>
 <li>
 Well versed with surgical procedures like cannulation of jugular, femoral, portal vein in rats. Chronic bile duct cannulations in rat and various animal models used for in vivo pharmacokinetics studies.
 </li>
 <li>
 Collection of different vital organs during tissue distribution studies in rodents
 </li>
 <li>
 Pharmacokinetic data analysis using Phoenix WinNonlin and other data analysis tool.
 </li>
 <li>
 Collection of various biological fluids like cerebrospinal fluid, bile, urine, synovial fluid lavage during PK and tissue distribution studies in rodents.
 </li>
 <li>
 Should have preliminary understanding about formulations to be used for pharmacokinetics, efficacy and toxicity studies.
 </li>
 <li>
 Knowledge about regulatory guidelines followed for drug metabolism and pharmacokinetics is desirable.
 
 
 </li>
 </ul>
 
 Behavioral Skills
 
 
 
 <ul>
 <li>
 Ready to take new challenges, committed to work and timelines
 </li>
 <li>
 Should have learning attitude, enthusiastic and hard working
 </li>
 <li>
 Good in communication skills
 </li>
 <li>
 Good work ethics.
 </li>
 <li>
 Good interpersonal skills, and the ability to work effectively in a team environment.
 </li>
 <li>
 Ability to work with co&hyphen;workers professionally.
 </li>
 <li>
 General ability to thrive in a growing infrastructure and culture and also think out of box approach when troubleshooting and problem solving.
 
 
 </li>
 </ul>
 
 Equal Opportunity Employer
 
 
 
 <ul>
 <li>
 It is the policy of Syngene to provide equal employment opportunity (EEO) to all persons regardless of age, color, national origin, citizenship status, physical or mental disability, race, religion, creed, gender, sex, sexual orientation, gender identity and/or expression, genetic information, marital status, status with regard to public assistance, veteran status, or any other characteristic protected by applicable legislation or local law. In addition, Syngene will provide reasonable accommodations for qualified individuals with disabilities.
 </li>
 </ul>
</div>

Invivo PK Scientist

GreyOrange

<div>
 
 As an Operations Research Scientist at GreyOrange, you will play a pivotal role in designing, developing, and implementing advanced models and algorithms that enhance decision-making across various business functions.
 
 
 You will collaborate with cross-functional teams, including product development, engineering, to optimize processes, improve product performance, and solve complex problems using mathematical and computational methods.
 
 
 The ideal candidate has a deep understanding of optimization, statistical modeling, and machine learning, with a passion for solving real-world problems in a high-tech environment.
 
 
 
 Roles and responsibilities:
 
 
 <ul>
 <li>
 Develop and apply advanced operations research models, including optimization, simulation, and stochastic models, to solve complex business challenges.
 </li>
 <li>
 Work closely with engineering, data science, and product teams to integrate OR models into SaaS platforms, providing actionable insights to enhance product performance.
 </li>
 <li>
 Design and implement optimization algorithms (e.g., linear programming, mixed-integer programming, and nonlinear optimization) to drive business efficiency and effectiveness.
 </li>
 <li>
 Analyze large datasets and develop predictive models using statistical and machine learning techniques to improve decision-making processes.
 </li>
 <li>
 Develop mathematical models, simulations, or optimization algorithms to represent real-world systems or problems.
 </li>
 <li>
 Use techniques like linear programming, dynamic programming, queuing theory, or game theory to create models.
 </li>
 <li>
 Build simulation models to evaluate potential solutions and improve resource allocation, scheduling, and supply chain logistics.
 </li>
 <li>
 Collaborate with stakeholders to understand business requirements and translate them into mathematical models and algorithmic solutions.
 </li>
 <li>
 Collaborate with cross-functional teams such as engineering, product, and operations to implement solutions.
 </li>
 <li>
 Communicate findings, insights, and recommendations to non-technical stakeholders through reports, presentations, or data visualizations.
 </li>
 <li>
 Communicate complex mathematical concepts and results to non-technical stakeholders in a clear and understandable manner.
 </li>
 <li>
 Monitor the performance of implemented solutions and refine models over time based on feedback and new data.
 </li>
 <li>
 Stay updated with the latest advancements in operations research, machine learning, and optimization, and apply best practices to develop innovative solutions.
 </li>
 <li>
 Document models, algorithms, and analytical processes for future reference and team knowledge sharing.
 </li>
 <li>
 Generate reports that summarize the outcomes of analyses and model performance.
 </li>
 </ul>
 
 
 Requirements/Qualifications:
 
 
 <ul>
 <li>
 Master&rsquo;s or Ph.D. in Operations Research, Applied Mathematics, Industrial Engineering, Computer Science, or a related field.
 </li>
 <li>
 Strong expertise in mathematical modeling, optimization techniques (linear programming, dynamic programming), and statistical analysis.
 </li>
 <li>
 Proficiency in programming languages such as Python, R, C++, and experience with optimization tools (Gurobi, CPLEX, MATLAB).
 </li>
 <li>
 Familiarity with data analysis, machine learning techniques, and simulation.
 </li>
 <li>
 Excellent problem-solving skills and the ability to translate complex problems into actionable insights.
 </li>
 <li>
 Strong collaboration skills and the ability to communicate complex concepts to non-technical stakeholders.
 </li>
 <li>
 Experience managing or contributing to research projects in a fast-paced environment.
 </li>
 </ul>
 
 
 About GreyOrange:
 
 
 <ul>
 <li>
 GreyOrange is a global leader in AI-driven robotic automation software and hardware, transforming distribution and fulfillment centers worldwide.
 </li>
 <li>
 Our solutions increase productivity, empower growth and scale, mitigate labor challenges, reduce risk and time to market, and create better experiences for customers and employees.
 </li>
 <li>
 Founded in 2012, GreyOrange is headquartered in Atlanta, Georgia, with offices and partners across the Americas, Europe and Asia.
 </li>
 </ul>
 
 
 Our Solutions:
 
 
 <ul>
 <li>
 The GreyMatter Multiagent Orchestration (MAO) platform provides vendor-agnostic fulfillment orchestration to continuously optimize performance in real time: the right order, with the right bot and agent, taking the right path and action.
 </li>
 <li>
 Currently operating more than 70 fulfillment sites across the globe (with deployments of 700+ robots at a single site), GreyMatter enables customers to decrease their fulfillment Cost Per Unit by 50%, reduce worker onboarding time by 90% and optimize peak season performance.
 </li>
 <li>
 In retail stores, our gStore end-to-end store execution and retail management solution supports omnichannel fulfillment, real-time replenishment, intelligent workforce tasking and more.
 </li>
 <li>
 Using real-time overhead RFID technology, the platform increases inventory accuracy up to 99%, doubles staff productivity, and enables an engaging, seamless in-store experience.
 </li>
 </ul>
 
 
 EEO:
 
 
 <ul>
 <li>
 GreyOrange provides equal employment opportunities to all employees and applicants for employment and prohibits discrimination and harassment of any type without regard to race, color, religion, age, sex, national origin, disability status, genetics, protected veteran status, sexual orientation, gender identity or expression, or any other characteristic protected by federal, state or local laws.
 </li>
 <li>
 This policy applies to all terms and conditions of employment, including recruiting, hiring, placement, promotion, termination, layoff, recall, transfer, leaves of absence, compensation, and training.
 </li>
 </ul>
</div>

Operations Research Scientist

Microsoft

<div>
 
 Overview
 
 
 
 We are looking for an outstanding Software Engineer to join the Microsoft Discovery team and work on an exciting new AI-driven platform that will transform the scientific research process. Microsoft Discovery is a cutting-edge enterprise platform that uses AI agents and high-performance computing to accelerate research and development (R&amp;D) in science and engineering. Microsoft Discovery is already driving breakthroughs in collaboration with partners across industries like chemistry and materials, energy, manufacturing, and pharmaceuticals, showcasing its potential to solve real-world challenges through AI-powered discovery.
 
 
 We innovate quickly, learn from data and experience, think creatively, and aren&rsquo;t afraid to laugh and have fun &ndash; all while tackling challenging customer and partner problems. You will be part of a startup-like environment within Microsoft, joining a highly motivated team that enjoys tough challenges and rapid development iterations. We collaborate closely with key platform teams across Microsoft and Microsoft Research to design, build, and operate the Discovery platform and related products.
 
 
 
 Responsibilities
 
 
 
 As a Software Engineer, you will:
 
 
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 Architect and build scalable, distributed systems from the ground up.
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 Drive features from ideation to global rollout.
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 Mentor engineers and lead design/code reviews.
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 Improve engineering velocity and service reliability through adoption of AI-enabled process enhancements.
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 Collaborate across Microsoft Research and platform teams to integrate cutting-edge technologies.
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 Apply site-reliability engineering practices to ensure robust operations.
 
 
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 Qualifications
 
 
 
 Required:
 
 
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 5+ years of experience in commercial software development.
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 Bachelor's degree in Computer Science or related field.
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 General awareness of building AI-powered services and multi-agent systems.
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 Proven experience building and operating reliable distributed systems.
 
 
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 Preferred
 
 
 
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 Experience with cloud platforms and enterprise-scale services.
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 Experience with widely-used scientific tools in chemistry, physics and bio sciences.
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 Strong problem-solving skills and a bias for action.
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 Passion for scientific innovation and cross-disciplinary collaboration.
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 Advanced degree (MS/PhD) is a plus.
 
 
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 This position will be open for a minimum of 5 days, with applications accepted on an ongoing basis until the position is filled.
 
 
 Microsoft is an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to age, ancestry, citizenship, color, family or medical care leave, gender identity or expression, genetic information, immigration status, marital status, medical condition, national origin, physical or mental disability, political affiliation, protected veteran or military status, race, ethnicity, religion, sex (including pregnancy), sexual orientation, or any other characteristic protected by applicable local laws, regulations and ordinances. If you need assistance with religious accommodations and/or a reasonable accommodation due to a disability during the application process, read more about
 
 requesting accommodations.
 
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Software Engineer -II

Qualitest

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 Data Scientist &ndash; QA &amp; AI Enablement (Hub Model)
 
 
 Role Summary
 
 
 
 We are seeking one Data Scientist to join the Hub component of the client hub-and-spoke engagement model. The Data Scientist will play a key role in supporting the deployment, refinement, and optimization of Coco bots and AI-driven quality engineering initiatives.
 
 
 
 Key Responsibilities
 
 
 
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 Participate in daily stand-up meetings, provide status updates, highlight risks, and deliver daily and weekly status reports.
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 Collaborate with business and technical stakeholders to gather representative and high-quality datasets for AI model development and testing.
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 Perform statistical analysis, data visualization, data cleansing, and preparation of datasets for AI/ML testing and validation.
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 Evaluate the effectiveness, reliability, and accuracy of new and existing data sources using appropriate data assessment techniques.
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 Apply predictive modeling techniques to improve QA efficiency and optimize broader business outcomes.
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 Coordinate with cross-functional teams to implement data models and monitor their operational outcomes.
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 Develop and maintain processes, metrics, and tools to continuously monitor model performance, data quality, and accuracy over time.
 
 
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 Required Skills &amp; Experience (Must Haves)
 
 
 
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 AI / Machine Learning: Proficiency level 4/5
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 Predictive Modeling: Proficiency level 4/5
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 Data Modeling Implementation: Proficiency level 3/5
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Data Scientist- QA and AI Enablement

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