With the growing emphasis on data-driven decisions and increasing business complexities, the demand for decision scientists continues to rise. These professionals bridge the gap between raw data and actionable insights. They enable businesses to make informed decisions based on real-time data.
But what does it take to excel in this role?
In this article, from novices to experienced decision scientists, these skills can lay the foundation for your success.
14 top skills required for a decision science job
Data Analysis
Data analysis refers to the technique of gathering raw data and transforming it into valuable insights that can be used further for informed decision-making. For a decision science job, possessing data analysis skills is a must.
As businesses increasingly rely on data-driven decisions, a proficient decision analyst’s ability to understand and manipulate data sets becomes necessary. Professionals should possess the understanding to discern the reasons and mechanisms behind specific business occurrences.
SQL
Structured query language (SQL) is a domain-specific language that enables professionals to manage and manipulate relational databases. In a decision science job, professionals often need to derive meaningful information from raw data.
SQL skills empower decision scientists to independently access, manipulate, and understand the data that informs their analyses. This independence and direct interaction with data enable more agile, informed, and effective decision-making. That’s why most companies prefer hiring candidates with strong SQL skills.
Programming and Data Manipulation
Decision science job revolves around data, analytics, and business strategy. A decision scientist role requires understanding complex business problems, framing them in analytical terms, and using data-driven methods to derive actionable insights. Having programming skills enables professionals to:
- Access diverse set of data sources for necessary information retrieval
- Convert raw data into clean, structured data for appropriate analysis
- Perform better decision-making by leveraging advanced analytics
- Scripting their workflows to guarantee consistent and repeatable outcomes.
SAS
SAS is a sought-after statistical software suite by the SAS Institute known for its advanced analytics, business intelligence, and predictive analysis capabilities. It has been a significant player in the analytics industry for decades.
Many large corporations and institutions heavily rely on SAS, which makes it an integral skill set for decision scientists to have. To land a great decision science job, professionals must have a clear understanding of the basic SAS programming language and be familiar with SAS datasets, SAS macros, and visualization techniques, among others.
PowerPoint
PowerPoint is a basic skill required to run daily errands in the decision science field. It helps decision scientists communicate complex insights to a diverse audience.
In a decision science job, professionals juggle multifaceted datasets and intricate analyses. While data nuances are necessary, they also need to effectively communicate the message to other stakeholders. PowerPoint is one such platform that is extensively used for this purpose.
Data Collection
Data collection is the systematic process of gathering relevant information from various sources to address specific research questions or business problems. For decision scientists, effective data collection is fundamental, as the quality and relevance of data directly impact the effectiveness of analyses and the validity of conclusions.
Decision scientists must be proficient in working with data collection tools, research design, sampling techniques, data validation, and other data collection practices.
BI
Decision scientists deal with deriving insights from complex data to drive strategic business decision-making. BI, short for Business Intelligence, also known as Business Analytics, involves the utilization of different tools, methodologies, and frameworks for better business decision-making.
Possessing BI skills in a decision scientist job enables candidates to effectively visualize, report, analyze, and present data in an easily digestible manner.
Analytical Support
Analytics support refers to a set of skills that includes logical thinking, research abilities, creativity, communication, data visualization, etc.
With these competencies, decision scientists can sift through vast amounts of data, discern patterns, and generate actionable insights. To land a decision science job, candidates must have excellent analytical support skills. They enable them to extract meaningful insights so that organizational decisions are both informed and impactful.
Business Rules
Business rules imply a set of rules that define the operational boundaries, standard operating procedures, and decision-making criteria within an organization.
Decision scientists with business rule knowledge are likely to offer data-driven recommendations and models aligned with the company’s operational realities and strategic intentions. A decision scientist must know how business rules work so that they can contribute to optimal business processes and create implementable strategies.
Regression
Regression skills are a key component of one’s decision-making abilities. This skill focuses on understanding the relationships between variables. Mastering regression techniques inform decision scientists about how certain factors influence outcomes. With this knowledge, they can better predict future results based on different input scenarios.
Furthermore, because regression provides quantifiable metrics to validate or challenge business intuitions, companies are always on the hunt for candidates with regression skills.
Analytical Tools
Analytical tools help decision scientists in data extraction, transformation, and visualization. They streamline what would otherwise be complex and time-consuming processes.
They promote faster and more efficient work processes and provide actionable insights to guide organizational strategies. Having proficiency in these tools enables decision-maker scientists to perform:
- Advanced statistical analyses,
- Predictive modeling,
- Machine learning.
Utilizing these tools, decision scientists can ensure that their findings are deep and comprehensive.
Subject Matter Expertise
Data is a reflection of real-world events and behaviors. Decision scientists possessing SME skills can gain a contextual understanding that ensures their analyses remain anchored in real-world scenarios and specific business implications.
This depth of knowledge enhances their ability to spot and interpret trends, anomalies, or outliers and strengthens their credibility among stakeholders. Decision scientists must have SME skills to produce results with elevated relevance and applicability of their data-driven insights.
Strategic Thinking
While technical skills help decision scientists decipher and understand data, strategic thinking ensures that this understanding is channeled into actionable, future-focused, and holistic business strategies.
Strategic thinking helps decision scientists align their data-driven insights with the long-term goals and visions of the organization. It also empowers them for risk management and allows them to see beyond immediate data patterns, anticipate market shifts, and propose solutions that keep the organization ahead.
Communication Skills
Apart from technical skills, soft skills are equally important. What sets good professionals apart from the rest is their ability to communicate their views and thoughts.
As in a decision science job, professionals need to distill complex analytical findings into clear, understandable, and actionable recommendations for stakeholders.
Having good communication skills can amplify the impact of their expertise. With good communication skills, they can present complex findings in a digestible manner so that decision-makers understand and capitalize on the derived insights.
Conclusion
If you’re planning to build a decision science career or want to switch to your dream decision science job, having these decision science skills can elevate your chances for success.
Throughout this article, we have discussed the 14 most essential decision-science skills that are your do-or-die. It’s important to note that, along with technical know-how, having business understanding and soft skills are equally required. You need to balance these skills for holistic development.
At last, keep learning about changing industry trends and advancements and keep growing!
FAQs
What skills do decision scientists need?
There are multiple skills required for a decision science job, including data analysis, SQL, Data visualization, knowledge of analytical tools, collaboration skills, critical thinking, and more.
What is a career in decision science?
Decision science is a promising industry that extends a wide range of great opportunities. Careers in a decision science job can range from business analyst to software developer, data analyst, data scientist, etc.
How much does a decision scientist make a year?
On average, the salary of a decision scientist ranges from 4 lac- to 17 lac per year.
What is the difference between a data scientist and a decision scientist?
Data scientists and decision scientists work in collaboration, with their main roles revolving around data. A data scientist aims to identify patterns and trends through statistical analysis. On the other hand, a decision scientist focuses on utilizing data to form strategic decisions.
What are different types of decision-making skills?
There are different types of decision-making skills, including intuition-based, rational, collaborative, and a combination of intuition and collaborative decision-making.
What qualifications do you need to be a decision analyst?
To get a decision scientist job, you need a Bachelor’s degree in a related field such as statistics, mathematics, information systems, economics, business, or computer science.
What skills do you need to make wise decisions?
To make wise decisions in your decision science job, you need strong analytical skills, critical thinking, emotional intelligence, problem-solving, research skills, risk assessment, ethical judgment, processing information skills, etc.