Data Scientist -Machine learning

Infosys

Bengaluru

Sur place

INR 1 000 000 - 1 500 000

Plein temps

14 jours+
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Résumé du poste

Infosys is seeking a Data Science professional in Bengaluru to lead end-to-end data science and machine learning projects. Responsibilities include designing and evaluating ML models, consulting with stakeholders, and mentoring team members. Ideal candidates should have a Bachelor's degree in engineering or computer science and strong Python skills with 5+ years of experience in data science.

Candidates should also be familiar with AI technologies and data science tools like NumPy, Pandas, and scikit-learn. This role requires excellent communication skills and the ability to work collaboratively in a hybrid environment.

Qualifications

  • UG education in Computers: BTECH / BSC / BCA.
  • 5+ years of experience in Data Science and Machine Learning.
  • Strong hands-on experience with Python.

Responsabilités

  • Lead end-to-end data science projects from discovery to deployment.
  • Design, develop, and evaluate ML models for business objectives.
  • Consult with stakeholders to define problem statements and success metrics.

Connaissances

Python for data science workflows
Machine Learning
Data Science
Stakeholder management

Formation

Bachelor of Engineering, BTech, BCA

Outils

NumPy
Pandas
scikit-learn
TensorFlow
PyTorch

Description du poste

Educational Requirements

Bachelor of Engineering, BTech, BCA

Service Line

Data Analytics Unit

Responsibilities
  • Technical Delivery Modeling Lead end-to-end data science and machine learning project execution from discovery to deployment-ready deliverables.
  • Design, develop, and evaluate ML models aligned to business objectives, ensuring robust performance and generalization.
  • Perform data exploration, feature engineering, and model selection to improve predictive accuracy and reliability.
  • Establish model validation approaches, track metrics, and document assumptions, limitations, and outcomes.
  • Consulting Stakeholder Management Partner with stakeholders to translate business problems into analytical frameworks and measurable success criteria.
  • Communicate insights and model results clearly to technical and non-technical audiences, enabling decision-making.
  • Drive solution recommendations with a focus on feasibility, scalability, and business impact.
  • Leadership Quality Provide technical guidance and mentorship to team members, promoting strong engineering and modeling practices.
  • Review code, experiments, and outputs to ensure quality, reproducibility, and maintainability.
  • Contribute to reusable assets, templates, and best practices for consistent delivery across initiatives.
Minimum Qualifications
  • UG education in Computers: BTECH / BSC / BCA (Computers must be included in UG).
  • 58 years of experience in Data Science, Machine Learning, and AI/ML solution delivery.
  • Strong hands‑on experience with Python for data science workflows and model development.
  • Proven ability to build, evaluate, and improve ML models using sound statistical and analytical techniques.
  • Experience working with stakeholders to define problem statements, success metrics, and actionable outcomes.
Additional Responsibilities
  • Experience leading teams or workstreams, including mentoring, technical reviews, and delivery ownership.
  • Strong proficiency with Python data science ecosystem (e.g., NumPy, Pandas, scikit-learn) and experiment tracking practices.
  • Exposure to deep learning or advanced ML techniques and frameworks (e.g., TensorFlow, PyTorch) where applicable.
  • Ability to design scalable solution approaches and collaborate effectively in a hybrid work environment.
  • Strong documentation and communication skills to present insights, trade-offs, and recommendations with clarity.
Technical and Professional Requirements
  • Technology->AI-Data science->Machine Learning
  • Technology->AI-Data science->PYTHON
Preferred Skills
  • Technology->AI-Data science->PYTHON
  • Technology->AI-Data science->Machine Learning
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