Pursuing Machine Learning while doing job

Efficient Strategies for Balancing a Full-Time Job and Pursuing a Career in Machine Learning

By Sonalisha Mohanty Category Machine Learning Reading time 6.5 mins Published on Feb 20, 2024

Learn Tips and Tricks to Ensure Work-Life Balance with Assured Career Success

The advent of globalization and the Industry 4.0 revolution has affected businesses worldwide. Such global changes create an emergency of reskilling and upskilling for the workforce from diverse industries. Despite the changing job market, many working professionals see a significant challenge ahead. To be precise, pros see many factors impacting work-life balance that result in the loss of jobs.

As per People Matters reports every four in ten corporate employees fear losing their jobs and experience increasing insecurities due to the advent of AI & ML models. Artificial intelligence has become an important skill for experts in any domain. Shaping a future-proofing AI career needs machine learning skills with hands-on experience.

Experts who are eager to enhance their careers by acquiring cutting-edge skills require a fundamental understanding of Machine Learning algorithms. Thus, enrolling in the industry-led AI & ML programs can become a great move by experts. Keeping the requirements aside, experts see many hurdles limiting them from pursuing proven careers. Being a working professional it is not easy to dedicate adequate time for shaping a machine learning career. Even highly experienced pros also fail to manage task deadlines and continue learning ML skills to rebuild their careers.

This blog will reflect on efficient strategies to balance a full-time job and shaping a career in ML.

Ways to Balance a Full-time Job and Pursuing a Proven Career in Machine Learning

An image shows a woman standing on the top of a building and balancing a weighing scale. The left side shows job balance, and the right side shows career.

1. Prioritize Your Tasks with Time Management Skills

Efficacy in prioritizing tasks you to the ultimate win-win situation in time management. Experts must learn to manage work and study side by side. Pursuing a career in ML needs attentive learning and full dedication. Hence, experts with lingering task burdens will need help in fulfilling their work and study targets. Experts must list their work and study tasks and prioritize them accordingly. Pros with better time management skills can understand the benefits of prioritizing their tasks duly.

Strategy: To attain this, you must take the steps –

  • Design a weekly work and study timetable
  • Plan your time in a justified way
  • Highlight peak, leisure, and hectic time periods with different colors
  • Communicate the timetable with your trainer & employer

2. Goal Setting

As you get promoted to higher positions, your responsibilities get more critical at the workplace. Thus, setting realistic goals is vital for experts. It helps in setting gaps between work commitments and learning goals. Having unattainable goals can degrade your work and learning efficiencies.

Strategy: To attain control over your targets and gain success, you must –

  • Set SMART goals (work & study)
  • Set a goal for time gaps
  • Seek guidance from seniors or trainers
  • Align learning goals with professional success

3. Understand ML Job Roles and Duties

Basic clarity of ML job roles and duties can help you choose the right career path. Upskilling with ML algorithms requires practical exposure to different business cases. Hence, one must upgrade his skill set with expertise in handling complex machine learning algorithms. Practical learning needs sufficient time to invest in live projects. Plus, it helps you assess your skills and whether you are eligible for a machine learning career with MNCs.

Strategy: For better results, you must –

  • Research various ML job roles across MNCs
  • Go through job descriptions
  • Conduct skills assessments
  • Decide on real-time upskilling with 100% live projects

4. Choose Companies that Invest in Employee Skills Development

Pursuing an ML career with a full-time job can be challenging for many. Yet, many working pros feel stressful work conditions limit their career progress. But, do you know many firms are taking steps to retain their employees with continuous upskilling? Yes. As per the LinkedIn Learning Report, around 83% of highly successful employers intend to build an employee-oriented culture with full scope for upskilling. Thus, you must check the firm’s full-time job strategies while applying for jobs.

Strategy: Here, you must take the following steps –

  • Apply for jobs at different MNCs through job portals
  • Check the company’s reviews and ratings
  • Visit their official website
  • Read their annual report (employee’s achievement and future goals section)
  • Check their ranking as people-centric organizations.

5. Choose Smart Work over Hard Work

Smart work always gets appreciation over hard work. Experts eager to pursue a career in ML must learn to balance work and life. Their work experience in their domain can help them learn ML skills faster and vice versa. Experts can smartly use their learning experience to handle work projects and excel.

Strategy: See the following steps –

  • Choose an ML training program in your work domain
  • Map the ML tools or trends that you have learned with workplace tasks
  • Utilize workplace project experience while learning

6. Transparent Communication with Teams and Employers

Corporate support helps a lot in pursuing a machine learning career. It demands clear and transparent communication with employers or teams. Experts become able to balance their work and life with great ease. You can develop confidence and leadership qualities in handling tasks simultaneously. It also expands professional networks and reduces workplace stress.

Strategy: You must take steps –

  • Communicate with employers, HR, and team leaders regarding tasks
  • Talk with seniors in case of work stress
  • Talk with HR in case you plan for a career change or transition
  • Discuss your learning plans and timelines with your teams and leaders.

7. Enroll in Flexible Online Courses in ML

An image shows a woman sitting in front of a Laptop and enrolling in a flexible online course in ML.

One can balance work and career transition in ML if one knows full-time job strategies and ways of upskilling. Flexibility is the key factor that ensures a good balance between work and study. Experts can opt for online courses in AI/ML with flexible schedules. So, one can choose preferable batch sessions (weekdays or weekends) for upskilling.

Strategy: For this, you must take the steps –

  • Research various industry-driven online courses
  • Check the batch times and other schedules
  • Go through their course curriculum
  • Check whether they provide placement support

Real-life Examples

Here are some real-life success stories that will clarify the work-life balance strategies.

1. Sayyam’s Journey in Balancing Work and Career as a Machine Learning Engineer

Sayyam, after completing three years as a Research Analyst in an MNC, felt career stagnancy. His promotion was halted for a year, and he was looking for a better move. Then, he found about growing trends in AI & ML models controlling the future job market. But his 9-5 job stress was causing continuous burnout. He was tensed over how to balance work and life. Gradually, he opted for a flexible AI/ML course online that helped him attain career success within a year. He opted for weekend classes, planned his upskilling, worked on live projects, and had great social networking with like-minded people. Finally, he cracked the ML Engineer job at Amazon.

2. Pranali’s Journey in Balancing Work and Career as a Machine Learning Cloud Architect

Pranali, after completing her software engineering, joined a start-up as a programmer. Her skills in programming languages and cloud applications were commendable. But, she was unhappy with her career as she had to work under continuous pressure. Plus, she felt underpaid as compared to her work and skills. After a while, she learned about the lucrative career scope for AI/ML experts worldwide and opted to take a course. Now, she had to deal with her tight work schedule. She made a weekly task schedule and utilized her time wisely. Enrolling in an online course was a wise move where she gained hands-on experience and used the same to cover her tasks at work. She was placed as an ML Cloud Architect at HCL almost a year later.

End Notes

Many ask, Is work-life balance a myth?

Pursuing a career in the ML field with a work-life balance is challenging. With strategic attempts and planning, one can maintain the desired balance. Hence, having a balanced work and personal life is possible. As said before, choosing a flexible online course can solve the issue. A Master’s in Computer Science: Data Science and AI can ensure a successful career transition. Experts eager to reshape careers in ML can opt for AI & ML specialization under this program.

It offers flexibility to learn to trend tools. Customized learning followed by hands-on experience with fresh capstone projects proves effective. From a job placement perspective, a such master’s degree with a globally recognized university accreditation and proper project experience validation is vital. So work-life balance is better to say that work-study balance is not a myth. It depends on your direction of learning choice and time management planning.


1. Is machine learning worthy enough to pursue a career?

Today’s digital world welcomes AI-driven job roles with updated skills. Experts with career stagnancy prefer switching to ML job roles, as they offer long-term career success. AI holds the world's future, and experts skilled in ML algorithms can conquer real success. MNCs worldwide announce wide openings in ML roles with lucrative pay scales.

2. What career works with machine learning?

A career in machine learning welcomes tech and non-tech experts in any domain. Thus, with a basic knowledge of domain disciplines and numerical skills, experts can switch to the ML field. It depends on the career desires and interests of experts eager for career transition in ML.

3. Can I get a job after completing a machine learning course?

Yes, anyone can get desirable jobs with the latest machine learning skills. From the job perspective, first, you have to opt for a course program with 100% placement help. Next, you can crack any ML job with hands-on expertise with the trending tools.