Analytical Voyages #4: career Path of Mounika Gali

From Finance Analyst to Data Analyst: A non-linear path to success

Author

Dharani R
October 10, 2023

We are back with yet another insightful episode of our ‘Analytical Voyages” series, where get a glance at the journey of experienced data scientists and analysts who have carved out a niche for themselves in Data science. In this episode, we are glad to introduce you to Mounika Gali, who started as an economist and finance major and navigated the data science space with a different perspective and perseverance for continued learning. She currently works as a Data and Analytics manager at CIBC, leading transformative projects and delivering exceptional results.

Key Highlights from The Interview

  1. Career Path

    From Economics to Financial Analytics to Data Analytics, Mounika Gali’s career path has been non-linear and a unique one. She understood the need for data-driven decisions in her first job at Robert Bosch and started leveraging the power of data analytics in her strategic decision-making. This is where she fell in love with Data Analytics and started honing her programming and technical skills.

  2. Adaptation In an Evolving Field

    The field of data science has evolved rapidly, with an increasing volume and variety of data. From her experience, staying relevant demands continuous learning and adaptation of important skills including advanced technical proficiency, machine learning and statistical knowledge, domain expertise, data wrangling, big data technologies, data visualization, collaboration, problem-solving, soft skills, continuous learning, project management, and ethical considerations.

  3. Promising Field of Study

    Mounika Gali believes that Generative AI holds incredible promise and has opened up a realm of possibilities in various domains. Generative AI has fascinated her with its creative capabilities.

  4. Recommendations for Aspirants

    For aspiring data scientists or engineers, mastering the fundamentals of statistics and programming is crucial. Taking data structures and algorithms courses, working on real-world projects, building a professional network, and utilizing online learning platforms like Coursera and EdX are recommended. Persistence, constant learning, and building a unique skill set are emphasized.

Excerpts from The Interview

Q: Can you describe your career journey in data, from your initial role to your current position? What were the key milestones in your career progression?

A: In the ever-evolving landscape of data analytics and data science, career journeys are seldom linear. They often involve twists, turns, and unexpected discoveries. My own career path in this field has been no exception, marked by pivotal milestones that have shaped my trajectory from a student of economics to a Data Analytics and Automation Manager at CIBC.

My journey began with a strong academic foundation. I pursued a Bachelor's degree in economics at Lady Shri Ram College. This phase instilled in me a love for numbers, quantitative analysis, and critical thinking – skills that would prove invaluable in my data career. Thereafter, I decided to pursue my Masters in Finance from SUNY Buffalo.

Landing the first job is never easy. After numerous applications and failed interviews, I landed my first job as a Financial data analyst for Robert Bosch. During my time here, I realized the importance of and transformative potential of data-driven insights. I also understood the power of data analytics tools like Power BI and Tableau and how they can be leveraged to drive strategic decision-making for any organization.

While working at Robert Bosch, my fascination for data continued to grow, prompting me to consider transitioning into core data analytics and data science roles. To make this leap, I recognized the paramount importance of programming skills. The world of programming was a big unknown for me, filled with strange syntax and unfamiliar concepts. I embraced the challenges, starting from scratch and gradually immersing myself in the world of coding. The transition was far from smooth. There were moments of frustration and self-doubt, but the spirit of perseverance pushed me forward. Online tutorials, coding boot camps, and late-night debugging sessions became my companions. Through trial and error, I began to unravel the intricacies of programming languages, particularly Python and R. With time, the lines between economics and data science began to blur. My foundation in economics provided a unique perspective when approaching data-driven problems, and my newfound programming skills allowed me to harness the power of data in ways I had never imagined. It was a harmonious marriage of analytical thinking and technical expertise.

I embarked on a new chapter in my career after honing my technical skills. My journey culminated in securing the role of a Data Analytics and Automation Manager at CIBC, which is another defining point in my career. In this role, I lead data analytics projects and implement automation solutions that have a substantial impact on the organization.

Q: What skills and knowledge areas do you believe were most crucial in advancing from a junior to a mid-level or senior data scientist/data engineer?

A: Advancing from a junior to a mid-level or senior data scientist/data engineer requires a combination of technical expertise, domain knowledge, soft skills, and a commitment to lifelong learning. It's a journey of continuous growth and adaptation in a rapidly evolving field. I tried to categorize all the skills that I think are crucial below:

1. Advanced Technical Skills: As you progress in your career, your technical skills must evolve. This includes deepening your expertise in programming languages such as Python and R, as well as mastering libraries and frameworks like TensorFlow, PyTorch, sci-kit-learn, and Spark. For data engineers, proficiency in tools like Apache Spark, Apache Kafka, Apache Airflow, and distributed computing platforms is essential.

2. Machine Learning and Statistical Knowledge: You should be able to choose and implement appropriate algorithms, perform model evaluation, and handle complex data scenarios. For data engineers, understanding data warehousing and ETL (Extract, Transform, Load) processes is crucial.

3. Domain Expertise: To be effective, you need to understand the specific domain you're working in. Whether it's healthcare, finance, marketing, or any other field, domain knowledge helps you ask the right questions and tailor your data solutions to address real-world problems.

4. Data Wrangling and Preprocessing: Data is often messy, and being able to clean, preprocess, and transform data effectively is a vital skill. This includes handling missing values, dealing with outliers, and preparing data for analysis or modeling.

5. Big Data Technologies: For data engineers, familiarity with big data technologies like Hadoop, Hive, and HBase is essential. The ability to work with large datasets efficiently is increasingly important.

6. Data Visualization: Communicating insights from data is a key part of the job. Being proficient in data visualization tools like Matplotlib, Seaborn, Power BI, or Tableau helps you convey complex findings effectively to both technical and non-technical stakeholders.

7. Collaboration and Communication: As you move up, you'll likely take on more leadership and collaboration responsibilities. Effective communication with team members, project managers, and business stakeholders becomes crucial.

8. Problem-Solving Skills: Senior roles often involve tackling complex, open-ended problems. Strong problem-solving abilities, including the ability to define problems, develop strategies, and execute solutions, are vital.

9. Soft Skills: These include critical thinking, attention to detail, time management, and adaptability. Soft skills become increasingly important as you work on more complex projects and collaborate with multidisciplinary teams.

10. Continuous Learning: The data field is ever-evolving. Staying updated with the latest technologies, tools, and industry trends is essential. Pursuing advanced degrees or certifications can also be beneficial.

11. Project Management: Senior roles may require you to oversee projects, so knowledge of project management methodologies and tools can be an asset.

12. Ethical Considerations: As you become more influential in decision-making, understanding ethical considerations in data collection, analysis, and usage is critical.

Q: How has the field of data science evolved during your career, and how have you adapted your skills to stay relevant in this ever-changing landscape?
A: The field of data science has experienced remarkable evolution during my career, and staying relevant has required a commitment to ongoing learning and adaptation. Over the years, there has been an exponential increase in the volume, variety, and velocity of data. Demand and need for good data scientists has evolved at a very rapid pace and programming skills have become a must. To stay relevant, I have honed my coding skills in R and Python as well as mastering libraries and frameworks like TensorFlow, PyTorch, scikit-learn, and Spark. We are living in the age of ChatGPT and generative AI and it marks the significance of constant upskilling.


Q: What advice do you have for aspiring data scientists or data engineers who are just starting their careers? Are there any resources, courses, or certifications you would recommend?

A: My advice for aspiring data scientists or data engineers who are just starting out would be to begin by mastering the fundamentals of statistics and programming, as these are the building blocks of data science and data engineering. I would also recommend taking at least one data structures and algorithms (DSA) course, as this would hone your programming and problem-solving skills. One book that I particularly recommend for DSA would be ‘Problem Solving with Algorithms and Data Structures’ by Brad Miller and David Ranum. I think it’s an excellent resource, even for beginners. Once you master the basics, move on to real-world projects and build a project portfolio. This will be very helpful in showcasing your skills when you attend interviews. Connect with professionals in the field and use platforms like LinkedIn to reach out and keep building your network. Also, there are multiple online platforms like Coursera, EdX, and Udacity which have courses in data science, AI, Machine learning etc. from top universities. So, do consider checking them out. Lastly, I believe everyone’s learning journey is unique, and there is no ‘one size fits all’ approach to success. But, with constant learning and persistence, I’m sure you will eventually build a rewarding career in data.


Q: As you've progressed in your career, have you noticed any particular specialization or niche within data science that you find particularly promising or interesting?

A: One specialization within data science that has captured my attention and holds incredible promise is Generative AI. ChatGPT, a specific application of Generative AI, has created a lot of buzz recently. Generative AI has opened up a realm of possibilities in various domains, and its potential applications are both fascinating and impactful. What particularly intrigues me about Generative AI is its ability to create content that is not only contextually relevant but also highly creative. From text generation models like GPT-3 to image synthesis techniques like StyleGAN, Generative AI has demonstrated an astonishing capacity to generate human-like text, art, music, and even entire personas.

Q: How do you balance the technical aspects of your role with soft skills like communication, teamwork, and project management? How important are these skills for career advancement in data science? 

A: Technical skills and soft skills are equally important for career advancement in data science. It goes without saying how important technical skills are for succeeding as a data scientist. However, soft skills are equally important because data scientists often need to convey complex findings and insights to non-technical stakeholders and translating technical jargon into easy-to-understand language becomes extremely important

You can Connect with Mounika Gali https://www.linkedin.com/in/mounikagali