•2 min read•from Data Science
Switching out of Data Strategy to Technical work
Our take
Transitioning from a data strategy role to a more technical position can be challenging, especially in a large consulting environment like the Big Four. While your current focus on data governance and architecture may not align with your interests, it's possible to pivot toward technical work in the next 6-12 months. To position yourself effectively, consider seeking opportunities to collaborate on technical projects, pursuing relevant training in AI and model development, and networking with professionals in roles you aspire to.
I work as a consultant at big 4. I got hired into the their AI & Data Analytics practice for the financial sector. I was brought in being told that I would be working on technical projects. However, my first project ended up being providing data strategy and architecture work.
I am now being further pushed into more data governance and product management work. These are areas that I have no interest in. And yet, I keep getting pushed into them. I don’t have a say since I’m still fairly new have to take what I get.
I want to know if I can eventually make a switch to a company else where in the next 6-12 months doing more technical work? Like actually building and validating models. Pushing them into production. I don’t have such exposure through work any way but I have been doing analytical work for a long time now. I’m not up to date with the new AI and AI agent stuff but I understand the theory well and have played around in sandboxes with them.
I would greatly appreciate any advice on how to best position myself for a pivot and if something like this can be done. I don’t want to become a data governance type of a person.
[link] [comments]
Read on the original site
Open the publisher's page for the full experience
Related Articles
- [D] ML researcher looking to switch to a product company.Hey, I am an AI researcher currently working in a deep tech company as a data scientist. Prior to this, I was doing my PhD. My current role involves working ok physics related problems and the project life cycle could be 2-4 years and the change comes in my company very slowly. The problems are quite interesting but because of the slow pace of development, I find myself getting often frustrated. As a byproduct, I don’t think that I am learning as much as I can. Because of these reasons, I want to move to a company where the development cycles are short and you have the flexibility to iterate and test quickly. Ideally a company which directly interacts with customers, like uber. The problem I am facing is that in the interview processes, a lot of these companies require you to have a lot of practical experience with AB testing type of approaches, especially in the senior roles that I am applying for. I think I can bring a lot of the table but I just don’t have much practical experience with the product experimentation. How do I convince people to give me a shot despite that? submitted by /u/wonder2man [link] [comments]
- Is working as a data scientist (ML focus) but not getting to interact with the business a common tradeoff, or is my company just weird?Prefacing this with the fact that I've been in this field for 3 years, across 2 different DS roles at my company. My company is huge and I know that often results in specialized roles, however getting a balance of business and technical exposure is much more difficult than I think it should be. My first role was heavily consulting-focused for DS work and very little building for production. I moved to a team with a more technical focus to make sure I didn't lose that skill set and it's very difficult to get work with an actual business stakeholder, and I'm now worried I'm going to get worse at that. I've tried to find ways to work that into the role and to go talk to people to help find projects but the manager does not seem to support that for the team, only for themselves and one of the leads. I really don't feel like this should have to be an either-or dichotomy, especially since so many areas can benefit from data science work but they don't always know where or what they can ask for. Technical skills are important but they mean nothing if you can't work with the business. Is this more common for the stats/ML side of DS work or do I just need to start job searching? submitted by /u/TaterTot0809 [link] [comments]
- Current role only does data science 1/4 of the yearTitle. The rest of the year I’m more doing data engineering/software engineering/business analyst type stuff. (I know that’s a lot of different fields but trust me). Will this hinder my long term career? I plan to stay here for 5 years so they pay for my grad program and vest my 401k. As of now I’m basically creating one xgboost model a year and just doing analysis for the rest of the year based off that model. (Hard to explain without explaining my entire job, basically we are the stakeholders of our own models in a way, with oversight of course). I’m just worried in 5 years when I apply to new jobs I won’t be able to talk about much data science. Our team wants to do more sexy stuff like computer vision but we are too busy with regulatory fillings that it’s never a priority. The good news is I have great job security because of this. The bad news is I don’t do any experimentation or “fun” data science. submitted by /u/Tenet_Bull [link] [comments]
- What domains are easier to work in/understandI currently work in social sciences/nonprofit analytics, and I find this to be one of the hardest areas to work in because the data is based on program(s) specific to the nonprofit and aren't very standard across the industry. So it's almost like learning a new subdomain at every new job. Stakeholders are constantly making up new metrics just because they sound interesting but they don't define them very well, or because they sound good to a funder, the systems being used aren't well-maintained as people keep creating metrics and forgetting about them, etc. I know this is a common struggle across a lot of domains, but nonprofits are turned up to 100. It's hard for me, even with my social sciences background, because the program areas are so different and I wasn't trained to be a data engineer/manager, I trained in analytics. So it's hard for me to wear multiple hats on top of learning a new domain from scratch in every new job. I'm looking to pivot out of nonprofits so if you work in a domain that is relatively stable across companies or is easier to plug into, I'd love to hear about it. My perception is that something like people/talent analytics or accounting is stabler from company to company, but I'm happy to be proven wrong. submitted by /u/lemonbottles_89 [link] [comments]
Tagged with
#generative AI for data analysis#Excel alternatives for data analysis#big data management in spreadsheets#real-time data collaboration#enterprise data management#big data performance#conversational data analysis#intelligent data visualization#data visualization tools#data analysis tools#data cleaning solutions#natural language processing for spreadsheets#financial modeling with spreadsheets#rows.com#self-service analytics tools#financial modeling#predictive analytics in spreadsheets#real-time collaboration#predictive analytics#self-service analytics