Calculus Jobs

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Mathematics Specialist - AI Trainer Project- Freelance
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Maths Associate Faculty
AADYAS JUNIOR COLLEGE —Hyderabad, Telangana
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Maths Faculty | For IIT And NEET
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Account Manager - B2B Sales
Airtel India —Andhra Pradesh
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Math Tutor (Grades 9–12)
Shining Ace —Remote
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8d
Maths Teacher
Thesis Group of Institutions —Jamshedpur, Jharkhand
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IIT/JEE MATHS (PART TIME)
Euro International School, Sector 84, Gurugram —Vatika India Sector 82 Gurgaon, Gurugram, Haryana
- ₹50,000 - ₹60,000 a month
- Part-time
- In-person
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Data Scientist

India Health Link Pvt Ltd
Bengaluru, Karnataka

Quick apply

Job details

Full-time

Benefits

Work from home
Flexible schedule

Qualifications

TensorFlow
Statistics
Azure
Statistics
Law
Doctoral degree
Computer Science
Kubernetes
Calculus
Big data
NumPy
Research
Master's degree
Pandas
AWS
Math
Docker
Machine learning
APIs
ETL
Agile
Data science
AI
Python

Full job description

The job is full-time with flexible working hours (overlap with EST required)

Job Summary

We need a Data Scientist with experience to lead the creation of cutting-edge data-driven solutions at our company. The best person for the job knows mathematical modeling, statistics, Machine Learning (ML), and Artificial Intelligence (AI). They are also used to working with current frameworks and large language models (LLMs). You will use your strong problem-solving, critical thinking, and research skills to plan and carry out complete data science projects, try out new methods, share your results, and maybe even come up with intellectual property that can be patented.

Some of your main duties

Research and new ideas:

Learn more about modern LLM orchestration frameworks and APIs (for example LangChain or equivalent), and work with proprietary and open source LLMs.

Encourage new ideas by testing, proving, and studying the viability of new data-driven goods or features.

Keep up with new technologies and trends in your field to make sure the company stays on the cutting edge of AI.

End-to-End Project Execution:

Take charge of data science projects from the time they are first thought of to the time they are put into action. This includes gathering requirements, modeling, and keeping an eye on performance.

Partner cross-functionally with Engineering, Product, and Business teams to define backlogs, prioritize work, and deliver data solutions incrementally.

Ensure all models and data pipelines adhere to strong quality control, governance, and continuous improvement standards within an Agile framework.

Model Development and Deployment:

Use both traditional machine learning (ML) and cutting-edge artificial intelligence (AI) methods to design, build, and improve advanced predictive and prescriptive models.

To make the model work better, use best practices for feature building and data transformation.

Use the best frameworks and methods (like prompt engineering and LLM fine-tuning) to solve hard data problems.

Mentorship and technical leadership:

Lead by example and push for data-driven decisions across the whole company.

Be a mentor to peer data scientists and AI engineers, helping them learn new skills and advance in their careers.

Push for best practices in code quality, version control, and being able to make changes again and again.

Design and Validation of Experiments:

Do thorough tests (A/B testing, hypothesis testing) to confirm how well the model works and how it affects the business.

Advanced statistical methods and data visualizations can help you share results with people who don't have the same level of professional knowledge.

Knowledge Sharing and Intellectual Property:

Put together and talk about study results at company seminars, outside conferences, or events for the industry.

Write for academic and business journals and look for ways to protect new ideas.

Write down findings, methods, and best practices to help the company learn more.

Continual Improvement:

Find ways to make data pipelines better, such as how they receive, handle, store, and analyse data, and then put those ideas into action.

Use DevOps and MLOps to make the model creation lifecycle easier and make sure that deployments happen all the time.

Skills and qualifications needed

Education: Master's or Ph.D. in computer science, statistics, math, data science, or a related field, or at least two years of work in a similar role.

Technical Knowledge – Programming: Skill with Python and related tools (such as NumPy, Pandas, scikit-learn, TensorFlow, and PyTorch).

Statistics & Math: Strong knowledge of advanced math concepts (such as linear algebra, calculus, and optimization) and statistical ideas (such as probability, hypothesis testing, and regression).

Machine Learning: Supervised, uncontrolled, reinforcement learning, and deep learning are some of the machine learning and artificial intelligence (AI) models that have been built and used successfully in the past.

Prompt & Feature Engineering: Proven track record of creating useful prompts for LLM-based solutions, along with strong feature engineering skills to improve model performance.

LLM & Generative AI Tools: Know how to use LangChain, LangGraph, OpenAI, Claude, Gemini, LLAMA, and other similar tools for big language model projects.

Infrastructure and Data Handling: Knowledge of data engineering techniques, such as ETL processes, big data solutions, and cloud-based platforms (AWS, Azure, or GCP).

It would be helpful to know about containerization (Docker) and management (Kubernetes).

Critical Thinking and Problem-Solving: The skill of being able to break down difficult issues into manageable steps and plan experiments to test theories.

Able to work with uncertainty and quickly come up with new ideas.

Communication and teamwork: You should be able to communicate clearly both in writing and in person, and you should be able to turn complicated studies into insights that are useful for business.

Leadership: Proven ability to lead cross-functional teams, get everyone on the same page, and get everyone to agree on something.

Publications and Patents: It's a plus if you have a history of writing in peer-reviewed journals, giving talks at conferences, or patenting your own work.

Why Should You Join?

Impactful Work: You'll be able to lead projects with a lot of attention and create new data products that give the whole organization real value.

Cutting-Edge Tools & Research: New AI frameworks, large language models, and cutting-edge computing tools are at your disposal.

Culture of Innovation: We urge people to try new things, learn from their mistakes, and share what they know through patent applications and publications.

Professional Growth: a mentor, ongoing training, and chances to show off your skills at classes, conferences, and meetups.

Flexible Working Hours: Tailor your work hours to fit your lifestyle and enhance your work-life balance.

Work From Home: Enjoy the flexibility of working from home to increase productivity and comfort.

Laptop Provided: A high-performance laptop to help you be productive and efficient in your work.

We want to hear from you if you are interested in AI, eager to learn, and ready to use cutting-edge technology to solve hard problems.

Quick apply

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