Data Scientist Opportunity 2025 | Mahindra is hiring Data Scientist . The working location is Kandivali Plant, India. Interested candidates can apply online as soon as possible. The detailed eligibility and application process are given below.
Data Scientist Job Openings At Mahindra | Kandivali Plant – Overview
| Company Name: | Mahindra |
| Job Role: | Data Scientist |
| Location: | Kandivali Plant |
| Work Mode: | On-site |
| Last Date to Apply: | ASAP |
Job Description
- We are looking for a BI developer having experience of 1 to 5 years, who will be responsible for designing various analytical reports on company data and delivering them to Stakeholders.
- The candidate will be working with business users, understanding their requirements and then Creating optimize query to accommodate business needs and then Presenting information through reports. Also responsible to mentor the other developers in Team in optimizing SQL statements as necessary and establishing best practices.
Skills Required:
- Master’s in science, Stats, ML, DS/ Bachelors in Engg, Tech – DS, Comp Sci
- Functional Skills:
- ‘Working knowledge of Data Science toolbox like: Python, SQL, Jupyter Notebook, Azure/AWS cloud, PySpark, TensorFlow, PyTorch, Keras, and other Big Data toolkit
- Deep understanding of AI/ML models with experience in LLM, Reinforcement Learning, Computer Vision, and Deep Learning
- Expertise in NLP, Computer Vision, Speech technology(STT, TTS)
- Behavioural Skills:
- ‘Ability to connect technology with business goals and outcomes.
- A hustler who constantly likes to go into design-build-deploy cycle for innovative new idea
- A Problem solving mindset for trying out new ideasResult orientation with execution excellence
- Customer Focus
- Weaving passion and energy at work
- Can Do and Will Do attitude
Responsibility:
- ‘Deploying AI/ML solution to productions and generating value from it.
- Developing End-to-End solutions with teammates and working with stakeholders, data team, business team and product team.
- Helping dev-ops team to deploy and integrate solution with existing systems
- • Data Quality and Availability: Ensuring the quality and availability of data for model training and validation can be a significant challenge. This includes dealing with missing, unstructured, or inconsistent data.
- • Model Performance: Developing models that perform well in real-world scenarios is a complex task. It requires rigorous testing, validation, and continuous optimization.
- • Scalability: Scaling AI solutions to handle large volumes of data and integrate with various business functions can be technically challenging.
- • Keeping Up with Rapid Advances: The field of AI is advancing rapidly. Keeping up with the latest research, techniques, and tools can be demanding.
- • Ethical and Regulatory Compliance: Ensuring that AI models are transparent, fair, and comply with all relevant regulations and ethical guidelines is a critical challenge. This includes understanding and mitigating potential biases in AI models.
