Unlock Your Data Career Potential: Navigating the Choices to Find the Perfect Fit
"If it scares you, it might be a good thing to try."—Seth Godin
Today I’m going to the touch the most asked topic of our community. And I have tried to automate the task of “choosing perfect fit career for you” right here 🤩
But wait, before jumping to navigate the path. I want you to read it aloud (may be inside you).
The ones who are crazy enough to think they can change the world, are the ones who do - Steve Jobs
Whats the perfect career in Data profession?
Two ways to solve this problem, right now:
Personal Career Consultation - Understanding your skills, behaviour, interest, passion and map it with market requirement and use my expertise to figure it out for you. Which I do provide, for that you can email me at fullstackdatascience9@gmail.com
You are responsible for your own career and I will be giving you right information to decide on it.
Confused?🤷
Here is my suggestion for you - “If I would have been at your place. First I would try to figure out myself and if anywhere felt its not working well. Just because its THE CAREER(Minimum 60% of my life ), I would have invested in Consultation without any second thought.”
However for this post, We will be following, #2 approach. So to solve this question we will look at the problem statement from different visions, but keeping same question “best suited career for ?”
Bigger Picture
Now I have prepared end-to-end AI product life cycle into 5 stages. The good part is in each of the stage, will be sharing what all roles are working for that particular stage and whats the work involvement percentage from each role.
Stage 1: Client Onboarding:
The client is introduced to the project and the team, and their requirements and goals are identified. This stage typically involves the Business Analysts (BA) and the Product Owner (PO). BAs help gather requirements and understand the client's needs, while the PO is responsible for aligning the project with the client's goals. BAs are typically responsible for around 5-10% of the project, while the PO is responsible for around 10-15%.
Stage 2: Data Collection and Preparation
Data is collected and cleaned, and any necessary annotation is done. This stage typically involves the Data Annotator, Data Analyst, and Data Engineer. The Data Annotator is responsible for annotating the data to make it usable for the model. Data Analysts are responsible for cleaning and preparing the data for use in the model, and the Data Engineer is responsible for ensuring that the data is stored and organised in a way that is accessible and usable by the rest of the team. These roles are typically responsible for around 15-20% of the project.
Stage 3: Model Development
Models are developed and tested by the Data Scientists and ML Engineers. Data Scientists typically use statistical methods to build the models, while ML Engineers focus on the implementation and deployment of the models, using Machine Learning, Deep learning etc. These roles are typically responsible for around 30-40% of the project.
Stage 4: Deployment and Monitoring
The models are deployed and monitored for performance. This stage typically involves the MLOps team, which is responsible for deploying and maintaining the models in production. Data and Analytics Manager, who essentially work from stage #2 itself and are responsible for monitoring the performance of the models and identifying any issues. These roles are typically responsible for around 20-30% of the project.
Stage 5: Optimisation and Maintenance
The team works to optimise and maintain the model over time. This stage typically involves the Data Science Architect, Research Scientist, Marketing Scientist, and Data Modeler. The Data Science Architect is responsible for overseeing the overall architecture of the model and ensuring that it is scalable and maintainable, which start from the very initial stage of DS life Cycle and also at this stage where lot of changes in the architecture is needed. Research Scientists and Marketing Scientists focus on understanding how the model is being used and identifying areas for improvement, while the Data Modeler is responsible for maintaining and updating the model over time. These roles are typically responsible for around 5-10% of the project.
Each role has its own set of benefits and downsides which is also important for you to know w.r.t to our question of “best career fit for you”. Don’t worry will be looking on those parameter as well!
More crucial roles such as the Data Security Analyst, Database Administrator, Quantitative Analyst, Big Data Engineer, and Research Scientist. These may not be involved in the project on a daily basis, but they “play so important role” in ensuring the security and integrity of the data, as well as providing insights and analysis on the data. I can talk about Quantitative Analyst/Researcher whole day, they are the most demanding and so much well paid roles with HFT (High Frequency Trading) Firms.
IITians apply for Quantitative Researcher roles and you already know, pay scale in newspapers🚨
Let me know if you would also like to understand information about these, comment “Want Part 2 of other curial roles” using below button.
Additionally, the Scrum Master is responsible for leading the project using the Agile methodology, ensuring that the team is working efficiently and effectively. The Subject Matter Expert (SME) provides domain-specific knowledge and insight to the team to ensure that the model is accurate and relevant. The Product Manager is responsible for overseeing the overall project and ensuring that it is on track to meet the goals and deadlines set out by the client. The Statistician provides statistical expertise and guidance to the team.
In summary, each role plays a critical role in the AI product life cycle and it is important to have a well-rounded team with a diverse set of skills to ensure that the project is successful. Collaboration and communication among team members is crucial, as well as a clear understanding of the client's goals and requirements.
Many times companies look for combination of roles from single person and that is happening alot, especially in startups. Hence you can expect cross connection roles as-well. But on Fundamental level this is what happens.
I hope now you are able to figure out at which stage of AI product you can “make dent”, I mean not literally but instead make more money, I mean, you got my point ✌️
In the next stage we will dig deep into the roles/profiles of those stages to understand “is that really good fit?” or “you have re-iterate through the above stages again?” .
Before that, if you haven’t been part of our DS elite community, here is the chance for you.
How does your Day looks like?
Anytime if we ever had word before, so you must know that how much I emphasis on knowing the role from real ground i.e., day to day work, roles and responsibilities.
Hence I have prepared good list of data and its supporting career, right here. Please read it thoroughly and try to map which suits best for you. At this stage of time don’t ever think about “how much money you can make in that role”, instead your focus should be on mapping it with your nature, passion, behaviour and aspirations.
MLOps
A typical day for an MLOps Engineer may include monitoring the performance of models in production, troubleshooting and resolving any issues that arise, and implementing new features and updates to the models. They may also work with the Data Science and ML Engineering teams to ensure that models are properly deployed and configured. They may spend a significant amount of time working with infrastructure and tooling, such as Kubernetes, Docker, and AWS. They also need to have a good understanding of the software development life cycle (SDLC) and CI/CD pipeline.
Benefits: MLOps Engineers ensure that models are deployed and maintained effectively, which can improve the performance and reliability of the models. They also help to streamline the deployment process, making it faster and more efficient.
Downsides: MLOps Engineers may not have as much experience with the technical aspects of model development and may not be as involved in the actual development of the models.
Business Analysts
Business Analyst may include gathering requirements from clients, working with the Product Owner to align the project with the client's goals, and communicating with other members of the team to ensure that everyone is on the same page. They may also spend time analysing data and creating reports to understand how the model is being used and identify areas for improvement.
Benefits: Business Analysts have strong communication and client-facing skills, which can help to ensure that the project is aligned with the client's goals and requirements. They also have experience in gathering and analyzing data, which can provide valuable insights into how the model is being used.
Downsides: Business Analysts may not have as much technical expertise as other team members, and may not be as involved in the actual development of the models.
Business Intelligence
A typical day for a Business Intelligence Analyst may include creating reports and dashboards to visualise data and identify trends and insights. They may also work closely with the Business Analysts and other team members to understand the client's needs and goals and align the project accordingly.
Benefits: Business Intelligence Analysts have strong analytical and visualization skills, which can provide valuable insights into how the model is being used. They also have experience working with data, which can help to identify areas for improvement.
Downsides: Business Intelligence Analysts may not have as much experience working with clients or understanding their needs, and may not be as involved in the actual development of the models.
Data Annotator
Data Annotator may include annotating data to make it usable for the model. They may also work closely with the Data Analysts and Data Engineers to ensure that the data is properly cleaned and prepared for use in the model.
Benefits: Data Annotators have experience working with data and know how to properly annotate it to make it usable for the model. They also have experience working with data annotation tools, which can make the process faster and more efficient.
Downsides: Data Annotators may not have as much experience working with clients or understanding their needs, and may not be as involved in the actual development of the models.
Data Analyst
A typical day for a Data Analyst may include cleaning and preparing data for use in the model, as well as working closely with the Data Annotators and Data Engineers to ensure that the data is properly cleaned and prepared for use in the model.
Benefits: Data Analysts have experience working with data and know how to properly clean and prepare it for use in the model. They also have experience working with data analysis tools, which can make the process faster and more efficient.
Downsides: Data Analysts may not have as much experience working with clients or understanding their needs, and may not be as involved in the actual development of the models. They also may not have as much experience with machine learning and statistical modeling.
Product Owner
A typical day for a Product Owner may include aligning the project with the client's goals and requirements, working with the Business Analysts to gather requirements, and communicating with other members of the team to ensure that everyone is on the same page. They may also spend time analysing data and creating reports to understand how the model is being used and identify areas for improvement.
Benefits: Product Owners have strong leadership skills and are responsible for aligning the project with the client's goals and requirements. They also have experience in gathering and analyzing data, which can provide valuable insights into how the model is being used.
Downsides: Product Owners may not have as much technical expertise as other team members, and may not be as involved in the actual development of the models.
Scrum Master
Scrum Master may include leading the project using the Agile methodology, ensuring that the team is working efficiently and effectively, and facilitating communication and collaboration among team members. They may also spend time analysing data and creating reports to understand how the project is progressing and identify areas for improvement.
Benefits: Scrum Masters have experience leading projects using the Agile methodology and can help to ensure that the team is working efficiently and effectively. They also have experience in gathering and analysing data, which can provide valuable insights into how the project is progressing.
Downsides: Scrum Masters may not have as much experience working with clients or understanding their needs, and may not be as involved in the actual development of the models.
Subject Matter Expert
SME may include providing domain-specific knowledge and insight to the team to ensure that the model is accurate and relevant. They may also spend time analysing data and creating reports to understand how the model is being used and identify areas for improvement.
Benefits: Subject Matter Experts have a deep understanding of the domain in which the model will be used and can help to ensure that the model is accurate and relevant. They also have experience in gathering and analysing data, which can provide valuable insights into how the model is being used.
Downsides: Subject Matter Experts may not have as much experience working with clients or understanding their needs, and may not be as involved in the actual development of the models.
Data Science Architect
A typical day for a Data Science Architect may include overseeing the overall architecture of the model and ensuring that it is scalable and maintainable. They may also spend time analysing data and creating reports to understand how the model is being used and identify areas for improvement.
Benefits: Data Science Architects have a strong understanding of the overall architecture of the model and can help to ensure that it is scalable and maintainable. They also have experience in gathering and analyzing data, which can provide valuable insights into how the model is being used.
Downsides: Data Science Architects may not have as much experience working with clients or understanding their needs, and may not be as involved in the actual development of the models.
Data Scientist
Sexiest DS job may involve analysing large sets of data using statistical techniques,ML, DL techniques for building and testing, predictive and generative models, and communicating findings to stakeholders. They may also spend time collecting and cleaning data, and collaborating with other teams to identify business problems that can be solved with data analysis.
Benefits: Data scientists are in high demand, and the field is growing rapidly. They have the opportunity to work on cutting-edge projects, and their work can have a direct impact on the success of a business.
Downside: Data scientists may have to work with large and complex datasets, and the work can be challenging and time-consuming. They also need to have a strong understanding of statistics and programming.
These are some examples of different roles and their typical day, responsibilities, benefits and downsides. It's important to note that the percentage and time allocation for each role may vary based on the specific project and organisation.
It's also important to note that these roles and responsibilities may overlap or change depending on the project and organisation. For example, a Data Engineer may also have responsibilities in the model development stage, while a Data Science Developer may also be involved in model deployment and monitoring.
It's also important to have clear communication and collaboration among team members to ensure that the project is successful and that all roles are working together efficiently. This includes regular meetings, progress reports, and goal-setting to keep everyone on the same page and to ensure that the project is on track to meet the client's needs and requirements.
Additionally, it is important to have a well-rounded team with a mix of different skills and expertise in order to effectively carry out an AI project from end to end. A diverse team with different perspectives and areas of expertise can help to identify potential issues and opportunities that may be overlooked by a team with a more homogenous skillset.
In short, each role plays a critical role in the AI product life cycle and it is important to have a well-rounded team with a diverse set of skills to ensure that the project is successful. Collaboration and communication among team members is crucial, as well as a clear understanding of the client's goals and requirements.
Making Sense?
Awesome! I will appreciate if you share on your social media, just tag me in that post, you will get my social media links at the end of the mail.
I have couple of more interesting roles in Data Career, which one should really explore as career options. We have already reached our words limit, but if you are interested to know about roles like - “Data Engineer, ML engineer, Data science Developer, AI Researcher, Product manager, Statistician, Data and Analytics Manager, Database Administrator, Quantitative Analyst, Big Data Engineer, Data Security Analyst, Research Scientist, Marketing Scientist, Data Modeler”.
Write in the comments “Want Part 2 of - How does day looks like?”
Thanks for great insight!