Hello Yash.

I'll keept it short.

Start by doing an formal 6-9 months training/course in Data Engineering where you can learn and practice Data engineering. the one which you can manage with your current job. It's quite possible.

A company name AlmaBetter provides really good placement after training learners in Data science.

check out their offerings

Absolutely, Ashish! Securing domain-specific jobs in Machine Learning and Data Science can indeed be a rewarding challenge. Here are some strategies to help you navigate this path:

Master Core Skills: Focus on building a strong foundation in Machine Learning and Data Science. Gain proficiency in programming languages like Python, and develop expertise in relevant libraries and frameworks such as TensorFlow, PyTorch, and scikit-learn.

Education and Certifications: Pursue formal education through relevant courses, certifications, or even a specialized degree in Data Science or Machine Learning. These credentials can help validate your expertise and make your resume stand out.

Hands-on Projects: Undertake practical projects that showcase your skills and problem-solving abilities. Develop projects that mirror real-world challenges in the domain you're interested in. A robust portfolio can capture the attention of potential employers.

Internships and Research: Gain practical experience by participating in internships or research projects. These opportunities provide valuable exposure to industry practices and allow you to work on meaningful projects under expert guidance.

Networking: Engage with professionals in the Machine Learning and Data Science domain. Attend conferences, workshops, and webinars to connect with experts and learn about the latest trends. Networking can open doors to job opportunities and mentorship.

Hackathons and Competitions: Participate in data science competitions and hackathons. These events not only enhance your problem-solving skills but also provide a platform to showcase your abilities to potential employers.

Customized Resumes and Cover Letters: Tailor your resume and cover letter to highlight your relevant skills and experiences. Address how your background and projects align with the specific needs of the domain you're targeting.

Online Presence: Maintain a professional online presence, particularly on platforms like LinkedIn. Share insights, articles, and projects related to Machine Learning and Data Science to demonstrate your passion and knowledge.

Continuous Learning: Stay updated with the latest developments in the field. Follow research papers, industry blogs, and online courses to keep enhancing your skills and knowledge.

Soft Skills: Develop strong communication and teamwork skills. Many data science projects require collaboration and effective communication of findings and insights.

Remember, Ashish, perseverance and a proactive approach are key. Each step you take brings you closer to your goal. Embrace challenges as opportunities for growth and learning. With dedication and a well-rounded skill set, you can indeed secure domain-specific jobs in Machine Learning and Data Science.

Best of luck on your journey,

If you have any queries feel free to connect with me

https://app.preplaced.in/profile/ayan-mullick

Dear Ashish,

I understand how challenging it can be for enthusiastic freshers to secure domain-specific jobs in Machine Learning and Data Science. But I want you to know that your enthusiasm and dedication are your strongest assets on this journey.

I was once in your shoes, and I know it might seem overwhelming at first. Here's what I learned along the way:

Start with the Basics: Building a strong foundation is like setting up a sturdy scaffold. Understand algorithms, statistics, and programming languages deeply before diving into specialization.

1. Education is Key: A degree in Computer Science, Data Science, or related fields can give you a head start. But don't hesitate to explore online courses or certifications. I found these resources immensely valuable in honing my skills.
2. Projects Reflect Your Passion: Remember, you're not just a candidate; you're a problem solver. Create personal projects that excite you. It could be anything – from predicting movie ratings to diagnosing diseases. These projects will speak volumes about your capabilities.
3. Embrace Challenges: Platforms like Kaggle offer real-world challenges. Participate with enthusiasm, even if you're unsure at first. The learning curve might be steep, but every challenge you tackle enhances your skills.
4. Build Relationships: Networking is more than just a buzzword. Attend meetups, conferences, and connect with professionals on platforms like LinkedIn. You'll be surprised how many doors open when you're part of a supportive community.
5. Internships: Your Training Ground: Landing a full-time role might seem daunting. That's where internships come in. They provide hands-on experience and often lead to permanent positions.
6. Tailor Your Story: Your resume and portfolio should tell your unique story. Highlight your journey, your projects, and the lessons you've learned.
7. Stay Curious: The field is ever-evolving. Stay curious, read research papers, and keep experimenting. Adaptability is your superpower.
8. Balance with Soft Skills: Technical skills are vital, but don't underestimate the power of communication, teamwork, and problem-solving abilities. These skills make you a well-rounded candidate.
9. Believe in Yourself: Amidst all the learning and challenges, don't forget your worth. Believe in your potential. Every small step contributes to your growth.

Remember, Ashish, you're not alone in this journey. We're all here to support each other. Embrace the learning process, and don't be afraid to ask for help. You've got this! Feel free to connect here : https://app.preplaced.in/profile/raghu-datta

Best wishes,

Raghu Datta