Data Scientist

Cerebri AI, Austin TX or Toronto ON or Washington DC

Turn data into revenue

About Cerebri AI

Cerebri AI CVX platform uses the best Artificial Intelligence ( AI ), Operation Research ( OR ), and software to provide what is required in our digital age: value a customer's commitment to a brand and related products. It then uses these insights to then drive the selling of product and services. We use AI to answer the fundamental questions of the digital age: Who talks to the customer? Who understands the customer? How do we do this at scale when we have millions of customers?

Cerebri AI CVX platform uses a 10-stage AI software pipeline manages & processes data intake thru to producing insights and actions & presenting them via our APIs, in our customers' systems, or our UX. Our AI pipeline's first five stages manage data intake, a crucial step in producing great insights. One customer journey ( CJ ) per customer means all models targeting CX and revenue KPIs and related next best actions ( NBAs ) use the same journeys.

We work with companies selling to over 200 million consumers and have 24 patents filed on the Cerebri AI platform. We now have 40 employees in three offices in Austin, Toronto, and Washington DC. Over 80% of the staff are in technical roles in data science and software engineering.

How do we do this? We hire the best data scientists, mathematicians, and software developers and work as a cross-disciplinary team/gang/clan. We work hard, laugh hard, and impress our peers and clients. Because we can. And because we want to. To learn more, visit cerebriai.com. In the meantime, if you think you have what it takes, give us a spin and upload resume.

"Cerebri AI was recognized as 2019 Cool Vendor for Customer Journey Analytics by Gartner."

The ideal candidate

The ideal candidate is adept at leveraging large data sets to find patterns and using modelling techniques to test the effectiveness of different actions. S/he must have strong experience using various data mining/data analysis methods, using a variety of data tools, building and implementing models, using/creating algorithms, creating/running simulations, and testing its real-time implication. S/he must be comfortable working with a wide range of stakeholders and functional teams, trading off design to help others.

Responsibilities for Senior/Principal Data Scientist

Design, develop, test, advocate, evangelize and build data-driven modeling approaches

Assess the effectiveness and performance of modeling and data enhancement techniques

Perform feature analysis

Develop ontology for key market segments

Develop outcome/event taxonomy for key business models

Coordinate with different functional teams to implement data engineering, models and monitor outcomes

Build utility code and handle miscellaneous support tasks

Documenting projects and maintaining project documentation

Qualifications

· Experience working with and creating data architectures

· Experience with artificial intelligence, natural language processing, machine learning

· Good understanding of selected machine learning techniques: Supervised/Unsupervised/semi-supervised Learning, SVM, Tree-based Methods, Neural Networks, Naive Bayes, k-NN, ensemble methods, CNN, RNN, NLP, Feature Engineering, hyperparameters optimization

· Knowledge of advanced statistical techniques and concepts

· Experience using statistical computer languages (Python, SQL, etc.) and conventional data science toolkits, such as PANDAS, Weka, NumPy, MATLAB

· Knowledge of professional software engineering practices and best practices for the full software development life cycle, including coding standards, code reviews, source control management, build processes, testing, and operations

· Excellent written and verbal communication skills

· Project management

· A drive to learn and master new technologies and techniques

Tools we use

· Confluence

· JIRA

· Spark

· Azure, AWS, Kubernetes

· Python, Scala

· Keras, Scikit-learn, PANDAS, Numpy

· Bit bucket, GitHub

· Jupyter Notebook

· Postgress, Parquet

· JavaScript, airflow, Pulsar

Nice to haves

· Ph.D. in operations research, applied statistics, data mining, machine learning, physics or a related quantitative discipline

· Experience in operations research, applied statistics, algorithmic complexity, RDS

· Experience with time Series, econometrics

· Experience with streaming systems

· Experience with Object-oriented modeling/MVC design patterns

Experience anomaly detection systems

Role:

Design, develop, test, advocate, evangelize and build data-driven products that help our customers improve business decisions. You will provide insight into analytic practices, design and lead iterative learning and development cycles.

Responsibilities

Understanding and worked with database systems.
Understanding and worked with machine learning algorithms.
Perform feature analysis.
Develop ontology for key market segments.
Develop outcome/event taxonomy for key business models.
Build utility code and handle miscellaneous support tasks.
Documenting software projects and maintaining project documentation.
Working in a team environment as well as working alone.

Qualifications

Experience with Big Data, artificial intelligence, natural language processing, machine learning and/or deep learning.
Python programming skills with two (2) years or more of Python experience.
Good verbal and written communication skills.
Knowledge of professional software engineering practices and best practices for the full software development life cycle, including coding standards, code reviews, source control management, build processes, testing, and operations.
Master's degree or six (6) years related work experience delivering quality code on time.

Tools we use...

Confluence
JIRA
Spark
Azure
Python
Keras
Bit bucket
Jupyter Notebook
Scala
PostGres

Nice to haves...

Experience in some subset of the following: Java, R, Python, SQL, Scala, Spark.
Ph.D. in operations research, applied statistics, data mining, machine learning, physics or a related quantitative discipline.
Deep understanding of statistical and predictive modeling concepts, machine-learning approaches, clustering and classification techniques, supervised learning, recommendation and optimization algorithms.

About Cerebri AI

Cerebri AI provides AI and machine learning solutions to help enterprises grow top line revenues by giving them a 1:1 relationship with their customers. We do this by processing internal and external customer data, and by determining the dollar value a customer places on the “value” of a vendor, products, assets, etc. We also monetize a critical variable in any revenue situation, the customer’s ability to pay, so things such as up-selling opportunities can be clearly scoped and delivered. We call the results Customer Value Indexes (CVIs) for brands, vendors, assets and financing.