data scientist

data analyst

analyst

software development engineer Jobs in Mangaluru

software development engineer

product analyst

analytical development scientist non phd

Biocon Biologics

<div>
 Roles &amp; Responsibilities:Execution of experiments for analytical method development/ qualification/ transfer, in-process/ stability sample testing, biosimilarity analysis and for evaluation of new high throughput formats of testing using chromatography (HPLC/UPLC), electrophoresis /capillary electrophoresis, UV spectrophotometer, Solo-VPE and other high-end analytical tools. Sample analysis in GC, Atomic absorption spectroscopy (AAS), Charged Aerosol Detector (CAD) etc. Glycan analysis.Sequential and logical planning of experiments. Record the experiment details (procedure, controls, analyses, and observation of the experiments) accurately and completely following good documentation practices in electronic laboratory notebook.Compilation, and interpretation of experimental data Communication of data to cross-functional teams, compilation of data for reports and participate in internal group meetings.Prepare documents such as method development reports, protocols, instrument operation procedures, author change controls and deviations.Perform literature survey of assays, support in experimental design based on historical data and troubleshooting of experimentsMaintain equipment and instruments in coordination with instrumentation team and support installation of new equipment and prepare Equipment/Instrument operating proceduresRegularly track inventory of consumables/ critical reagents and making sure they are ordered and follow-up of the ordered material and support in laboratory maintenance.Participate in QMS related activities.
 
 Education:Degree in life sciences such as biology, biochemistry, biotechnology, analytical chemistry or closely related fieldM.Pharma/M.Sc/M.Tech with 3-8 years of research experience
 
 Required Experience:Experience in leading biopharmaceutical organizations, contributing to high-impact research and development initiatives.Strong expertise in chromatographic techniques including Ion Exchange (IEC), Reverse Phase (RP), Size Exclusion (SEC), and Hydrophilic Interaction Chromatography (HILIC) for protein therapeutics. Glycan analysis. Additional experience in excipient analysis using ELSD, CAD, and MS platforms is highly valued.Hands-on experience with a wide range of analytical instruments, such as HPLC, UPLC, Capillary Electrophoresis (CE), UV-Vis Spectrophotometer, SoloVPE, Octet, Maurice, GC and AAS etc. Higher order structure analysis techniques (CD spectroscopy, DSC, DLS, SEC-MALS, FTIR). Demonstrated ability to contribute to experimental design and troubleshooting within collaborative, cross-functional team environments.Skilled in data analysis using instrument-specific software, with working knowledge of statistical tools for method evaluation and interpretation.Thorough understanding of regulatory guidelines including ICH, WHO, and USP for method development, qualification, transfer, and validation. Proficiency in electronic laboratory notebook (eLN) systems is also preferred.Effective communicator and team player, capable of articulating scientific concepts, challenges, and solutions clearly.Familiarity with Empower Method Validation Manager is an added advantage
</div>

Analytical Development Scientist (Non PhD)

Amazon

<div>
 
 Description
 
 
 
 Have you ever ordered a product on Amazon and when that box with the smile arrived you wondered how it got to you so fast? Have you wondered where it came from and how much it cost Amazon to deliver it to you? If so, the WW Amazon Logistics, Business Analytics team is for you. We manage the delivery of tens of millions of products every week to Amazon&rsquo;s customers, achieving on-time delivery in a cost-effective manner.
 
 
 We are looking for an enthusiastic, customer obsessed, Sr. Applied Scientist with good analytical skills to help manage projects and operations, implement scheduling solutions, improve metrics, and develop scalable processes and tools. The primary role of an Operations Research Scientist within Amazon is to address business challenges through building a compelling case, and using data to influence change across the organization. This individual will be given responsibility on their first day to own those business challenges and the autonomy to think strategically and make data driven decisions. Decisions and tools made in this role will have significant impact to the customer experience, as it will have a major impact on how the final phase of delivery is done at Amazon.
 
 
 Ideal candidates will be a high potential, strategic and analytic graduate with a PhD in (Operations Research, Statistics, Engineering, and Supply Chain) ready for challenging opportunities in the core of our world class operations space. Great candidates have a history of operations research, and the ability to use data and research to make changes.
 
 
 This role requires robust program management skills and research science skills in order to act on research outcomes. This individual will need to be able to work with a team, but also be comfortable making decisions independently, in what is often times an ambiguous environment.
 
 
 
 Responsibilities May Include
 
 
 
 <ul>
 <li>
 Develop input and assumptions based preexisting models to estimate the costs and savings opportunities associated with varying levels of network growth and operations
 </li>
 <li>
 Creating metrics to measure business performance, identify root causes and trends, and prescribe action plans
 </li>
 <li>
 Managing multiple projects simultaneously
 </li>
 <li>
 Working with technology teams and product managers to develop new tools and systems to support the growth of the business
 </li>
 <li>
 Communicating with and supporting various internal stakeholders and external audiences
 
 
 </li>
 </ul>
 
 Basic Qualifications
 
 
 
 <ul>
 <li>
 3+ years of investigating the feasibility of applying scientific principles and concepts to business problems and products experience
 </li>
 <li>
 PhD, or Master's degree and 5+ years of quantitative field research experience
 </li>
 <li>
 Experience with big data technologies such as AWS, Hadoop, Spark, Pig, Hive etc.
 </li>
 <li>
 Experience communicating qualitative research methods and findings to non-qualitative researchers
 
 
 </li>
 </ul>
 
 Preferred Qualifications
 
 
 
 <ul>
 <li>
 Experience converting research studies into tangible real-world changes
 </li>
 <li>
 Experience with discrete and continuous optimization methodologies and algorithms
 
 
 </li>
 </ul>
 Our inclusive culture empowers Amazonians to deliver the best results for our customers. If you have a disability and need a workplace accommodation or adjustment during the application and hiring process, including support for the interview or onboarding process, please visit https://amazon.jobs/content/en/how-we-hire/accommodations for more information. If the country/region you&rsquo;re applying in isn&rsquo;t listed, please contact your Recruiting Partner.
 
 
 
 
 Company
 
 - ADCI - Karnataka
 
 
 Job ID: A3126640
</div>

Sr. Research Scientist, Last Mile Science

Intuit

<div>
 
 Overview
 
 
 
 Intuit is seeking a Sr AI Scientist to join our AI Science team in Intuit India for the Virtual Expert Platform (VEP) Organization.
 
 
 We&rsquo;re investing in a culture of &ldquo;builders&rdquo; by fostering an environment where innovation, creativity, and bold thinking are encouraged. I believe that the &ldquo;builder&rdquo; mindset is necessary for us to accelerate our move toward AI-native experiences. This requires more builders who can rapidly apply AI and emerging technologies to solve our customers&rsquo; problems.
 
 
 Join the Virtual Expert Platform team, the driving force behind Intuit's expert service, sales, and workforce management (and other Operations Research topics), generating billions in revenue.
 
 
 
 Responsibilities
 
 
 
 <ul>
 <li>
 Practices leadership and communication skills to influence teams and to evangelize AI science across the organization
 </li>
 <li>
 Collaborates with stakeholders to define success criteria and align ML model metrics with business goals. Works side-by-side with product managers, software engineers, and designers in designing experiments and minimum viable products
 </li>
 <li>
 Leads technical work of a scrum team: initiating and designing model solutions, driving end-to-end architecture designs of the team&rsquo;s work, and holding the team accountable for high quality code, git, design, costs and implementation standards
 </li>
 <li>
 Performs hands-on data analysis and modeling with large data sets, including discovering data sources, getting data access, cleaning up data, and making them &ldquo;model-ready&rdquo;. You need to be willing and able to do your own ETL and design/build featurization.
 </li>
 <li>
 Applies data mining, NLP, and machine learning (such as supervised/unsupervised, Causal-ML, Online Learning, Bayesian Learning, Reinforcement Learning, or Deep Learning) to real-world problems and datasets.
 </li>
 <li>
 Runs A/B tests to draw conclusions on the impact of your team&rsquo;s work and communicates results to peers and leaders
 </li>
 <li>
 Communicates with partners to ensure successful delivery and integration of DS solutions.
 </li>
 <li>
 Proactively researches, explores, and enables new AI/ML technologies. Keeps up with the new developments in academia and industry and considers possible extensions to solve Intuit customer problems.
 
 
 </li>
 </ul>
 
 Qualifications
 
 
 
 <ul>
 <li>
 MS or PhD in an appropriate technology field (Computer Science, Statistics, Applied Math, Operations Research, etc.).
 </li>
 <li>
 2+ years of experience with data science for PhD and 5+ years for Masters.
 </li>
 <li>
 Experience in modern advanced analytical tools and programming languages such as R or Python with scikit-learn.
 </li>
 <li>
 Efficient in SQL, Hive, or SparkSQL, etc.
 </li>
 <li>
 Comfortable in Linux environment
 </li>
 <li>
 Experience in data mining algorithms and statistical modeling techniques such as clustering, classification, regression, decision trees, neural nets, support vector machines, anomaly detection, recommender systems, sequential pattern discovery, and text mining.
 </li>
 <li>
 Solid communication skills: Demonstrated ability to explain complex technical issues to both technical and non-technical audiences
 
 
 </li>
 </ul>
 Intuit provides a competitive compensation package with a strong pay for performance rewards approach. This position will be eligible for a cash bonus, equity rewards and benefits, in accordance with our applicable plans and programs (see more about our compensation and benefits at Intuit&reg;: Careers | Benefits). Pay offered is based on factors such as job-related knowledge, skills, experience, and work location. To drive ongoing fair pay for employees, Intuit conducts regular comparisons across categories of ethnicity and gender.
</div>

Senior AI 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

<div>
 
 Overview
 
 
 
 Come join our collaborative and creative group of AI scientists and machine learning engineers for Intuit Foresight! As AI Scientist, you'll get to design and build next generation AI systems that directly impact our customers deploying machine learning models in AI-native apps, using everything from analytical
 
 
 algorithms to deep learning and generative AI. We're excited for you to join us and do some seriously awesome work.
 
 
 The team is at the heart of building next generation AI systems and algorithms using state of the art algorithms, platform infrastructure and massive datasets unique to Intuit. Building these AI and GenAI systems involve the use of large language models, reinforcement learning, human prefernce learning, forecasting, optimization and in-depth knowlege of using experimentation methods and AI evaluations.
 
 
 
 Responsibilities
 
 
 
 <ul>
 <li>
 Perform hands-on data analysis and modeling with huge data sets. Apply AI and and machine learning methods to build next generation AI Capabilities and Algorithms
 </li>
 <li>
 Work side-by-side with product managers, software engineers, and designers in designing experiments and minimum viable products
 </li>
 <li>
 Create and refine features from the underlying data. You&rsquo;ll enjoy developing just enough subject matter expertise to have an intuition about what features might make your model perform better, and then you&rsquo;ll lather, rinse and repeat
 </li>
 <li>
 Run regular A/B tests, gather data, perform statistical analysis, draw conclusions on the impact of your optimizations and communicate results to peers and leaders
 
 
 </li>
 </ul>
 
 Qualifications
 
 
 
 <ul>
 <li>
 MS or PhD in Computer Science, Mathematics, Statistics, Engineering, or related quantitative field
 </li>
 <li>
 2+ years of proven experience in building AI systems using Large Language Models and Reasoning Language Models as well as applying ML methods such as classification, regression, decision trees, deep neural nets, reinforcement learning. Hands on experience in running large scale AI Evals.
 
 
 </li>
 </ul>
 Intuit provides a competitive compensation package with a strong pay for performance rewards approach. This position will be eligible for a cash bonus, equity rewards and benefits, in accordance with our applicable plans and programs (see more about our compensation and benefits at Intuit&reg;: Careers | Benefits). Pay offered is based on factors such as job-related knowledge, skills, experience, and work location. To drive ongoing fair pay for employees, Intuit conducts regular comparisons across categories of ethnicity and gender. The expected base pay range for this position is:
</div>

AI Scientist - 2

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

Thermo Fisher Scientific

<div>
 
 Work Schedule
 
 
 
 Standard (Mon-Fri)
 
 
 
 Environmental Conditions
 
 
 
 Adherence to all Good Manufacturing Practices (GMP) Safety Standards
 
 
 
 Job Title: Scientist III, Protein Biology
 
 
 
 
 Job Location: India, Bangalore
 
 
 
 
 About Company:
 
 
 
 
 About The Company
 
 
 
 Thermo Fisher Scientific Inc. (NYSE: TMO) is the world leader in serving science, with annual revenue of approximately $44 billion. Our Mission is to enable our customers to make the world healthier, cleaner, and safer. Whether our customers are accelerating life sciences research, solving analytical challenges, growing efficiency in their laboratories, improving patient health through diagnostics, or developing and manufacturing life-changing therapies, we are here to support them. Our distributed team delivers an outstanding combination of innovative technologies, purchasing convenience, and pharmaceutical services through our industry-leading brands, including Thermo Scientific, Applied Biosystems, Invitrogen, Fisher Scientific, Unity Lab Services, Patheon, and PPD. Our Innovative Laboratory technologies &amp; Services makes it easier for customers.
 
 
 
 About Team: Antibody team
 
 
 
 The antibody team is involved in engineering and development of antibodies, with a deep focus on reagent quality and specificity. The core activities of team include design, development, purification, conjugation and testing of antibodies in a variety of immunoassay applications using pioneering tools and technologies. The goal of the program is advanced product development and creation of product specific information to promote research use.
 
 
 
 Role &amp; Responsibilities
 
 
 
 
 Role:
 
 
 
 
 To be an integral part of an antibody development team with a focus on performance and specificity using innovative molecular, protein, and cell biology methods.
 
 
 
 
 Responsibilities:
 
 
 
 <ul>
 <li>
 Independent experimental planning and execution to meet program goals and schedules.
 </li>
 <li>
 Timely record keeping of results into data sheets and laboratory note-books.
 </li>
 <li>
 Regular mining of literature and keeping oneself up to date with developments in field of antibody development and protein biology. Ideate effectively to recommend improvements in product development and workflows.
 </li>
 <li>
 Compilation and presentation of data in written and oral formats to different audiences including stakeholders and leadership. Active participation in team meetings.
 </li>
 <li>
 Mentoring junior scientists and provide in-lab supervision.
 
 
 </li>
 </ul>
 
 Candidate Requirement:
 
 
 
 
 Education &amp; Experience levels:
 
 
 
 <ul>
 <li>
 Applicants should have Master&rsquo;s with greater than 15 years confirmed R&amp;D experience or PhD with greater than 7 years&rsquo; experience in a life sciences field with a strong focus on molecular biology, protein design &amp; purification, immunology, biotechnology and/or biochemistry. Minimum 3-4 years of Industrial experience is required.
 
 
 </li>
 </ul>
 
 Nature of experience:
 
 
 
 <ul>
 <li>
 Extensive experience and knowledge in protein purification and analysis technologies such as affinity or ion exchange chromatography, SEC, HPLC, and SDS-PAGE. Experience and knowledge of protein affinity measurement platforms and Immunoassays.
 </li>
 <li>
 Extensive experience and familiarity with antibody or protein engineering,
 </li>
 <li>
 Experience with antigen design for immunizations, analysis and identification of epitopic regions desirable.
 </li>
 <li>
 Familiarity or experience in development of conjugation chemistries or performing antibody conjugation.
 </li>
 <li>
 Theoretical or practical experience in antibody development platforms is desirable.
 </li>
 <li>
 Project or work experience in support of relevant skills such as publications, project reports or thesis work.
 </li>
 <li>
 Proficient in MS Office applications such as Word, PowerPoint, Excel and possess a strong statistical knowledge and familiarity with statistical software such as Prism.
 
 
 </li>
 </ul>
 The successful candidate will be self-motivated and a self-starter who is excellent at learning and applying new concepts for process or product improvements, possess excellent oral and written communication skills to connect to peers and leadership, and will be an excellent teammate with the ability to work with different colleagues.
 
 
 Thermo Fisher Scientific is an equal opportunity employer and value diversity at our company. All qualified applicants will receive consideration for employment without discrimination basis race, color, religion, sex, sexual orientation, gender, gender identity or expression, national origin, protected veteran status, age, genetics, pregnancy, disability or any other legally protected status. Thermo Fisher Scientific aligns with all applicable equal employment opportunity legislation in each jurisdiction in which it operates.
 
 
 We offer competitive remuneration, annual incentive plan bonus, healthcare, and a range of employee benefits. Thermo Fisher Scientific offers employment with an innovative, forward-thinking organization, and outstanding career and development prospects. We offer an exciting company culture that stands for integrity, intensity, involvement, and innovation!
 
 
 ThermoFisher is committed to crafting a diverse work environment that values and respects individuals from all backgrounds. Thermo Fisher Scientific is an EEO/Affirmative Action Employer and does not discriminate on the basis of race, color, religion, sex, sexual orientation, gender identity, national origin, protected veteran status, disability or any other legally protected status.
 
 
</div>

Scientist III

Explore 501+ Analytical development scientist non phd in Mangaluru this January. Find roles across top companies and apply now to boost your career.