•1 min read•from Data Science
Current role only does data science 1/4 of the year
Our take
Balancing roles in data science, data engineering, and business analysis can be challenging but also enriching. Your current focus on data science for just a quarter of the year may seem limiting, yet your experience in software engineering and analysis is valuable. As you plan for the next five years, consider how to leverage your diverse skill set to enhance your marketability.
Title. 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.
[link] [comments]
Read on the original site
Open the publisher's page for the full experience
Related Articles
- What has been people's experience with "full-stack" data roles?I started my career being a jack of all trades - hired as a data analyst but I had to extract, clean, and then analyze data and even sometimes train models for simple predictions and categorization. That actually led me to become a data engineer but I've spent most of my career working closely with data scientists and trying my best to make their jobs easier by taking away all the preprocessing tasks away from them so they can focus on training, inference MLops, etc. While I claim to have helped them, to be honest DE teams often become a bottleneck and an obstacle. Everything from not being able to provide the data needed to train on time, or how we processed the data was wrong and led to bad performance, or they went live with a model blindly because we couldn't get them the observation data on time for them to analyze accuracy. I'm wondering how much of the data engineering tasks can be automated/vibed away by data scientists. My guess is that in larger companies this won't be the case but I think startups and SMBs want to move fast so they'd rather have data scientists own the whole pipeline. What has been other's experience with this and where is it heading? submitted by /u/uncertainschrodinger [link] [comments]
- Is the ds/ml slowly being morphed into an AI engineer? [D]Agents are amazing. Harnesses are cool. But the fundamental role of a data scientist is not to use a generalist model in an existing workflow; it's a completely different field. AI engineering is the body of the vehicle, whereas the actual brain/engine behind it is the data scientist's playground. I feel like I am not alone in this realisation that my role somehow got silently morphed into that of an AI engineer, with the engine's development becoming a complete afterthought. Based on industry requirements and ongoing research, most of the work has quietly shifted from building the engine to refining the body around it. Economically, this makes sense, as working with LLMs or other Deep Learning models is a capital-intensive task that not everyone can afford, but the fact that very little of a role's identity is preserved is concerning. Most of the time, when I speak to data scientists, the core reply I get is that they are fine-tuning models to preserve their "muscles". But fine-tuning is a very small part of a data scientist's role; heck, after a point, it's not even the most important part. Fine-tuning is a tool. Understanding, I believe, should be the fundamental block of the role. Realising that there are things other than "transformers" and finding where they fit into the picture. And don't even get me started on the lack of understanding of how important the data is for their systems. A data scientist's primary role is not the model itself. It's about developing the model, the data quality at hand, the appropriate problem framing, efficiency concerns, architectural literacy, evaluation design, and error analysis. Amid the AI hype, many have overlooked that much of their role is static and not considered important. AI engineering is an amazing field. The folks who love doing amazing things with the models always inspire me. But somehow, the same attention and respect are no longer paid to the foundational, scientific side of data and modeling in the current industry. I realise it's not always black and white, but it's kind of interesting how the grey is slowly becoming darker by the day. Do you feel the same way? Or is it just my own internal crisis bells ringing unnecessarily? For those of you who have recognized this shift, how are you handling your careers? Are you leaning into the engineering/systems side and abandoning traditional model development? Or have you found niche roles/companies that still value the fundamental data scientist role (data quality, architectural literacy, statistical rigor)? I'd love to hear how you are adapting submitted by /u/The-Silvervein [link] [comments]
- Does this sound like a real Data Scientist role, or more like analytics/enterprise software support?I recently got hired into a Data Scientist role at my current company (aerospace/supply chain), and I’m trying to get a better sense of how people would classify the work. In my previous role as a Data Analyst, I was more on the business development/analytics side. I worked on things like Tableau dashboards, SQL/Python analysis, market and proposal support, parts forecasting, and some NLP/ML-style projects for predicting parts or work classification (being taken over by a separate team). So this new role is aligned with forecasting, asset management, and supply chain decision support. Does seem like DS but I’m not 100% sure. The new role is focused on service parts planning, forecasting, repair recommendations, proposal support, and working with an enterprise planning/optimization system. The thing I’m unsure about is that the core modeling is mostly handled inside specialized software called Servigistics. I wouldn’t be building the main forecasting or optimization models from scratch in Python. A lot of the work would be updating model inputs, running/supporting the system, analyzing outputs, explaining changes in the forecasts or recommendations, answering stakeholder questions, and building dashboards or analysis using SQL, Python, Tableau, and Excel. I’m thinking about doing some lightweight analysis around the system, like leveraging Monte Carlo simulation for risk/uncertainty, forecast validation, bias analysis, and maybe using internal training material with a RAG/LLM setup to help with process support and onboarding. I want to do this to make sure I’m able to showcase I still possess strong technical skills for future opportunities. Would you consider this a legitimate applied Data Scientist role even if I’m not hand-building models from scratch? Or does this sound more like an operations analyst/business analyst role with a Data Scientist title? I’m not trying to be overly picky. I just want to make sure this is a good role to grow in for the next year or two and that I’m not moving into something that will box me out of more traditional data science work later. One other thing is the job change did come with a 25% bump in pay which is a big reason why I took it, along with the title change. Thank you. submitted by /u/miquiztli8 [link] [comments]
- How are you all navigating job search as a data scientist?I feel ineligible for about 70% of the posted job advertisements since they all ask about Agentic/LLM stuff. I have worked with these tools and do use them at work. It's just that it's not my main job that I do on daily basis and I don't want to exaggerate my experience around these tools. I have about 10+ years of work ex and have actually worked from just data scientist to combination of ML and data engineer. submitted by /u/proof_required [link] [comments]
Tagged with
#generative AI for data analysis#Excel alternatives for data analysis#conversational data analysis#data analysis tools#big data management in spreadsheets#real-time data collaboration#intelligent data visualization#data visualization tools#enterprise data management#big data performance#data cleaning solutions#natural language processing for spreadsheets#financial modeling with spreadsheets#rows.com#digital transformation in spreadsheet software#business intelligence tools#cloud-based spreadsheet applications