What are the most important skills for R&D statisticians to collaborate with other departments?

As we see the entire R and D process and its success is depend on the skills of the R&D statisticians. it is they who dig out the research findings and interpret these properly. They need to softly kindly step by step aware all the R and D team as well as other departments. So that all will accept his perspective and be ready for the subsequent changes required to make your product unique. They should have to play the role of baby, father and grandfather as and when required while communicating. Thus in my own view the most important skills for R&D statisticians is to be the most loved and loving one.

I agree, domain knowledge is the most important aspect. Moreover, communicating with the research scientists and their requirements for type of analysis required is essential. Sometimes statistician can recommend better analysis if they have good domain knowledge.

Conhecimento de domínio é uma importante habilidade que os estatísticos de P&D precisam ter para entender as particularidades envolvidas e poder contribuir melhor com outros departamentos.

To foster effective collaboration across departments, cultivating a profound understanding of our industry's context, objectives, and obstacles is pivotal. This expertise empowers you to shape insightful research inquiries, decipher findings, and deliver pertinent, actionable insights to stakeholders. Nurturing domain knowledge demands curiosity, proactivity, and a commitment to soliciting input from experts and users. Staying ahead of contemporary trends, alongside gleaning wisdom from best practices and case studies, is equally essential for sustained proficiency in our field/domain.

The domain knowledge preferred here is not the technical domain knowledge as a statistician. It's the subject matter expert (SME) from processes where data are generated. Therefore, go to the Gemba (Japanese term for where actions happen) to learn first hand rather than from interviews as second-hand information because whatever SMEs say requires verification to determine to what degree are these second-hand information are valid to what certainty. ?? I had statistians who didn't do Gemba and became messengers rather than to provide 1st-hand information, so when they interpreted the statistical results, those analysis didn't correspond with SMEs. To be curious and proactive, the Gemba walk is to acquire primary knowledge. ??

One thing, I found helpful with statisticians who are either in your area of expertise or not, to explain the problem to the basics, patiently work with them until they have complete clarity of your question. You will be surprised to see the solutions they come up with and even empower your study design significantly. Engaging with statisticians way early on in the study during inception or initial stages of the study is the key to have a very productive design for your study or trial. It is also about teamwork and how efficiency you can engage them and update them of the study development in a timely manner.

I met statistician who had perfect communication style. He was: 1. very patient, especially with Dummies 2. Was familiar with the industry, i.e. the subject, of his statistical study 3. started each presentation with repeated definitions and explanations of the terms used

A vital, good communication skill for R&D statistians is to simplify the complexity from technical results without coming-across as a show off that I know my stat. ?? A better communication is to make tangible, actionable recommendation based on what the data suggest rather than to state, "The data suggest we no longer do this." This can only occur if one has proactively done Gemba walk. ???? Otherwise, stating the obvious such as cease and decease from doing the current process according to the data is definitely not helpful. ?? That's not exactly the tangible & actionable recommendation to earn one a high respect from upper management. ??

Effective communication is a key for statistitians. Without the right way of visualisation of the data will make it confusing and not passing the right message.

For researchers and statisticians, being good at explaining things is crucial. It means one can easily share our ideas, methods, and results with different groups. This skill helps you build trust with our team and make sure everyone is on the same page. To get better at it, practice adjusting your message based on who we are talking to, and use clear language and examples. Don't forget to listen well and ask questions to make sure everyone understands.

One quote is good enough to define teamwork.. "If you want to go fast - go alone. But if you want to go far go with your team."

For statisticians in research, working well in a team is key. It means being good at collaborating with people from different departments. Teamwork helps us use everyone's strengths and ideas, and deal with any issues that come up. To get better at teamwork, be respectful and supportive, share your goals and responsibilities, and be ready to adapt and learn from your team.

To have good problem-solving skill, start with the basic. It's call the problem statement. A good problem statement must contain three elements. 1) What's the defect or issue (not plural here to demonstrate that the drill down has been done). 2) What's the magnitude of this problem to demonstrate how severe or small is this issue. 3) What's the trend to show whether this isolated event is severe or these little nuisances has gone in the wrong direction. Out of all your peers, please refrain from being the first to inform your boss that "We have new issues" as if you're on top of your game, or you know all new problems under your watch. That's not impressive. Start to impress your boss with a good problem statement. ??

There is no problem during the work, it's only casualties. Something will always be broken, some people will need attention and deadlines are usually expired before you know that it's needed to be done. So be calm, solving problems is always part of the job.

Problem-solving skills empower R&D statisticians to effectively collaborate with other departments by facilitating interdisciplinary understanding, clear communication, and the ability to customize statistical approaches. They contribute to data-driven decision-making, process optimization, conflict resolution, and innovative problem resolution, fostering adaptability in dynamic collaborations.

Essentially setting clear learning objectives, utilizing diverse and credible resources, actively seeking feedback, and reflecting on one’s progress are fundamental strategies to foster a culture of continuous learning. This approach not only benefits the individual’s career trajectory but also significantly contributes to the overall innovation and success of the R&D team. In essence, the ability to learn and grow continuously is a key component of an R&D statistician’s toolkit, enabling them to keep pace with the ever-changing landscape of data science and to drive meaningful collaborations.

While I agree that having technical expertise and great communication skills are an absolute must , one should also emphasize that other skills are quite important: namely, networking (relationship building and maintenance), being empathetic (includes showing vulnerability and providing psychological safety), and also being self-aware are important when cross-collaborating in a scientific industry.

Sales, sales, sales! Learn what your customers want and need. Innovation is good, innovation that fills a need or solves a pain is better.