LTIMindtree is looking for a Data Scientist who will support building AI tools for customers The candidate should be skilled at using large data sets to find opportunities for product and process optimization The candidate must have a proven ability to drive business results with their databased insights The candidate must be comfortable working with a wide range of stakeholders and functional teams

Primary Skills Python, AI, ML (Machine Learning) Gen AI – Generative AI + RAG, NLP, LLM, Deep Learning, Computer Vision, AWS, Cloud Or Azure Open AI (Any Cloud)

Experience- 5 to 16 Years

FACE 2 FACE DRIVE - MUMBAI (POWAI)
DATE - 12TH MARCH 2025

If anyone is interested please share your updated resume on email id - Devyani.Pillai2@ltimindtree.com

Capital One hiring Senior Manager, Machine Learning Engineering

Location: Bengalore

Link: https://capitalone.wd12.myworkdayjobs.com/Capital_One/job/Bangalore-In/Senior-Manager--Machine-Learning-Engineering_R211489

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Complete Roadmap to land a Data Scientist job in 2025

Phase 1: Build Foundations (3-6 months)

1. Learn Python programming basics
2. Understand statistics and mathematics concepts (linear algebra, calculus, probability)
3. Familiarize yourself with data visualization tools (Matplotlib, Seaborn)

Phase 2: Data Science Skills (6-9 months)

1. Master machine learning algorithms (scikit-learn, TensorFlow)
2. Learn data manipulation frameworks (Pandas, NumPy)
3. Study data visualization libraries (Plotly, Bokeh)
4. Understand database management systems (SQL, NoSQL)

Phase 3: Practice and Projects (3-6 months)

1. Work on personal projects (Kaggle competitions, datasets)
2. Participate in data science communities (GitHub, Reddit)
3. Build a portfolio showcasing skills

Phase 4: Job Preparation (1-3 months)

1. Update resume and online profiles (LinkedIn)
2. Practice whiteboarding and coding interviews
3. Prepare answers for common data science questions

Best Resources to learn Data Science 👇👇

Python Tutorial

Data Science Course by Kaggle

Machine Learning Course by Google

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Interview Process for Data Science Role at Amazon

Python Interview Resources

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How to get job as python fresher?

1. Get Your Python Fundamentals Strong
You should have a clear understanding of Python syntax, statements, variables & operators, control structures, functions & modules, OOP concepts, exception handling, and various other concepts before going out for a Python interview.

2. Learn Python Frameworks
As a beginner, you’re recommended to start with Django as it is considered the standard framework for Python by many developers. An adequate amount of experience with frameworks will not only help you to dive deeper into the Python world but will also help you to stand out among other Python freshers.

3. Build Some Relevant Projects
You can start it by building several minor projects such as Number guessing game, Hangman Game, Website Blocker, and many others. Also, you can opt to build few advanced-level projects once you’ll learn several Python web frameworks and other trending technologies.

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4. Get Exposure to Trending Technologies Using Python.
Python is being used with almost every latest tech trend whether it be Artificial Intelligence, Internet of Things (IOT), Cloud Computing, or any other. And getting exposure to these upcoming technologies using Python will not only make you industry-ready but will also give you an edge over others during a career opportunity.

5. Do an Internship & Grow Your Network.
You need to connect with those professionals who are already working in the same industry in which you are aspiring to get into such as Data Science, Machine learning, Web Development, etc.

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Do these 4 things to 10x your responses while asking for referrals:

1. Be personal. (never use AI)

I get a ton of messages that are either written by AI or obviously copy and pasted to 100 people.

Be personal by mentioning something you have in common with the person you’re messaging or what you got out of one of their posts.

2. Have a specific job that you want to apply for and send the link.

“Can you look and see if there are any openings?” is incredibly rude and inconsiderate of the person’s time.

If you want them to help you with a referral, do the work for them by sending them the link, why you’re a good fit, and other needed info.

3. Reach out to people who are active on LinkedIn, but not content creators.

Everytime there’s an opening at my company, I get 50 messages asking for a referral. As much as I want to, I can’t refer everyone.

Therefore, look for those to connect with at a company you’re interested in that post occasionally on LinkedIn, but are not content creators.

These people will be active enough to see your message, but not have 3 dozen other messages asking for the same thing.

4. Build relationships way before you ask for a referral.

While I don’t do many referrals bc of how many inquiries I get, I’d be much more likely to refer someone who adds to the conversation by commenting on my posts, creates good posts themselves, and overall seems like a smart, nice person.

Doing this turns you from a complete stranger to a friend.

I know a lot of people are pressed for time on here, but building relationships is what networking is all about.

Do that effectively and your network may offer you referrals when there’s an opening.

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Check out this job at OnPoint Insights : https://www.linkedin.com/jobs/view/4206595599

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United Airlines is hiring Data Scientist 🚀

Qualification : Bachelor's degree
Experience : 0-3 Years
Location : Gurugram

Apply link : https://careers.united.com/us/en/job/UAIUADUSGGN00001951EXTERNALENUSTALEO/Associate-Data-Scientist?utm_source=linkedin&utm_medium=phenom-feeds

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Dell is hiring for multiple data science roles

https://jobs.dell.com/en/employment/bengaluru-karnataka-india-data-science-and-analytics-jobs/375/77091/1269750-1267701-1277331-1277333/4

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Difference between linear regression and logistic regression 👇👇

Linear regression and logistic regression are both types of statistical models used for prediction and modeling, but they have different purposes and applications.

Linear regression is used to model the relationship between a dependent variable and one or more independent variables. It is used when the dependent variable is continuous and can take any value within a range. The goal of linear regression is to find the best-fitting line that describes the relationship between the independent and dependent variables.

Logistic regression, on the other hand, is used when the dependent variable is binary or categorical. It is used to model the probability of a certain event occurring based on one or more independent variables. The output of logistic regression is a probability value between 0 and 1, which can be interpreted as the likelihood of the event happening.

Data Science Interview Resources
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Baazi Games is hiring Data Analyst 🚀

Experience : 1+ Year
Location : Delhi

Apply link : Check out this job at Baazi Games: https://www.linkedin.com/jobs/view/4209194116

I am looking for a talented and driven Data Scientist to lead Loyalty Analytics in Mumbai (work from office)

Ideal candidate:

• ~5 years of hands-on experience in Loyalty, Marketing, or Growth Analytics
• Strong analytical thinking, problem-solving skills, and the ability to derive insights from data to drive business decisions
• Proficiency in data tools and technologies (e.g., Python, SQL, R, etc.)
• Experience working with large datasets and building scalable analytical solutions

You’ll work closely with cross-functional teams to design data-driven strategies that enhance customer engagement and retention.

Please share your CV at rahul.dasgupta@idfcfirstbank.com