What does an AI Architect do?
Data scientists utilize their analytical, statistical, and programming skills to collect, analyze, and interpret large data sets. They then use this information to develop data-driven solutions to difficult business challenges. Data scientists commonly have a bachelor's degree in statistics, math, computer science, or economics. Data scientists have a wide range of technical competencies including: statistics and machine learning, coding languages, databases, machine learning, and reporting technologies.
- Work with stakeholders throughout the organization to identify opportunities for leveraging company data to drive business solutions.
- Mine and analyze data from company databases to drive optimization and improvement of product development, marketing techniques and business strategies.
- Assess the effectiveness and accuracy of new data sources and data gathering techniques.
- Develop custom data models and algorithms to apply to data sets.
- Use predictive modeling to increase and optimize customer experiences, revenue generation, ad targeting and other business outcomes.
- Develop company A/B testing framework and test model quality.
- Coordinate with different functional teams to implement models and monitor outcomes.
- Develop processes and tools to monitor and analyze model performance and data accuracy.
- Strong problem solving skills with an emphasis on product development.
- Experience using statistical computer languages (R, Python, SLQ, etc.) to manipulate data and draw insights from large data sets.
- Experience working with and creating data architectures.
- Knowledge of a variety of machine learning techniques (clustering, decision tree learning, artificial neural networks, etc.) and their real-world advantages/drawbacks.
- Knowledge of advanced statistical techniques and concepts (regression, properties of distributions, statistical tests and proper usage, etc.) and experience with applications.
- Excellent written and verbal communication skills for coordinating across teams.
- A drive to learn and master new technologies and techniques.
- We’re looking for someone with 5-7 years of experience manipulating data sets and building statistical models, has a Master’s or PHD in Statistics, Mathematics, Computer Science or another quantitative field, and is familiar with the following software/tools:
- Coding knowledge and experience with several languages: C, C++, Java,
- Knowledge and experience in statistical and data mining techniques: GLM/Regression, Random Forest, Boosting, Trees, text mining, social network analysis, etc.
- Experience querying databases and using statistical computer languages: R, Python, SLQ, etc.
- Experience using web services: Redshift, S3, Spark, DigitalOcean, etc.
- Experience creating and using advanced machine learning algorithms and statistics: regression, simulation, scenario analysis, modeling, clustering, decision trees, neural networks, etc.
- Experience analyzing data from 3rd party providers: Google Analytics, Site Catalyst, Coremetrics, Adwords, Crimson Hexagon, Facebook Insights, etc.
- Experience with distributed data/computing tools: Map/Reduce, Hadoop, Hive, Spark, Gurobi, MySQL, etc.
- Experience visualizing/presenting data for stakeholders using: Periscope, Business Objects, D3, ggplot, etc.
How much does an AI Architect make near United States?
AI Architect Career Path
Learn how to become an AI Architect, what skills and education you need to succeed, and what level of pay to expect at each step on your career path.
Years of Experience Distribution
AI Architect Insights
“Excellent leadership and a motivated team make Intellibonds a fantastic firm from which to advance a career.”
“The domain is incredibly fascinating and the chance to work with metallurgists is really awesome.”
“It's got some great people working on some really interesting and cutting edge issues.”
“I really enjoyed the opportunity to experiment and try new/state of the art techniques in the DS team”
“Whilst the growth and development in skills is great the career path clarity is difficult to navigate.”
“There are quite a few reviews saying promotions are political and I just don't see that.”
“Cafeteria didn't provide great range in cuisines and passes the hygiene test just above average.”
“This area is fun to work in and I can really see how we can help our organization.”
AI Architect Interviews
Frequently asked questions about the role and responsibilities of data scientists
Data scientists combine math, computer science, analytics, and business acumen to interpret large amounts of data to help businesses devise future strategies. They determine how the data can be used to achieve a company's targets.
Data science is a great career. More and more organizations are employing data scientists to use data analysis to create online frameworks, implement machine learning techniques, and stay at the forefront of data science innovation.
Data scientists are valuable to businesses, so they are very well paid. The average base pay for a data scientist in the United States is $171,275. At the highest level of education and experience, they can earn up to $217,854.
Data science is a challenging job. Analyzing vast amounts of data and coming up with solutions to better organizations can put a great deal of pressure on these individuals. However, the rewarding nature of this career makes it worthwhile to work as a data scientist.