The data scientist will take on complex requests and transform them into clean, data solutions while integrating it with the architecture used across the company. We are looking for a hands-on data enthusiast and ML practitioner passionate about data quality and automation. This role will also be actively involved in evaluating and implementing new tools, and frameworks or extending existing ones by leveraging the on premise and cloud services while supporting the company’s data science, data warehousing and visualization initiatives.
This role will be based in Syndigo's office in downtown Chicago.
Key responsibilities and activities:
- Champion the effort to design and implement algorithmic solutions at scale that deals with unpredictable data patterns while maintaining and monitoring data integrity.
- Passionate about automation and work towards identifying opportunities for increasing efficiencies across the organization.
- Partner with cross functional teams to identify, analyze, and interpret trends or patterns (outliers, anomalies) in complex data sets and make recommendations.
- Communicate findings from exploratory and predictive data analysis broadly to data stewards and the data custodians.
- Create Insights via dashboards and reports to aid in reporting KPIs and optimizing workflows
- Be the visible face of the data science team to internal partners, external customers and prospects, and the data scientist community at large
- Deep understanding of supervised, unsupervised machine learning techniques (clustering, decision tree, neural networks, etc.) and their real-world advantages/drawbacks.
- Lead all phases of the end to end Machine Learning Pipeline/Lifecycle: data preparation, cleansing, modeling and fine-tuning to build accurate, robust, and reliable statistical results
- Expertise in data structures and algorithms with expertise in working with structured, semi structured and unstructured datasets
- Deep experience with R or Python, SQL and running ETL jobs; exposure to additional languages preferred.
- Proficient understanding of distributed computing principles and integration of data from multiple data sources and formats
- Leverage data mining, statistics, and machine learning to develop best-in-class analysis techniques & data visualizations that answer strategic client / category questions
- Knowledge of various ETL techniques and frameworks, such as Flume, Talend, Spoon and various messaging systems, such as Kafka
- Understanding of the theory and application of Continuous Integration/Delivery
- Experience with SQL & NoSQL databases, such as MSSQL, PostGres, MongoDB, Cassandra, Neo4J
- Good understanding of cloud platforms (Azure/ AWS) and serverless Architecture, along with its advantages and drawbacks
- Advanced Degree (PhD or Master’s) in Computer Science, Applied Mathematics, Engineering, or equivalent.
- 4+ years in ML Engineering working with structured and unstructured data formats.
- Demonstrated knowledge and experience with development and deployment of models in on premise and cloud environments.
- Strong consultative skills to establish relationships across the broader organization
- Comfort communicating and interacting with cross functional teams, as well as understanding and translating the science of data to a more general audience
- Desire to “roll up the sleeves” and get into the heart of the business
- Demonstrable ability to work across a global matrix and ability to put strong communication, leadership and influencing skills to work to be successful
Equal Opportunity Employer/Protected Veterans/Individuals with Disabilities
The contractor will not discharge or in any other manner discriminate against employees or applicants because they have inquired about, discussed, or disclosed their own pay or the pay of another employee or applicant. However, employees who have access to the compensation information of other employees or applicants as a part of their essential job functions cannot disclose the pay of other employees or applicants to individuals who do not otherwise have access to compensation information, unless the disclosure is (a) in response to a formal complaint or charge, (b) in furtherance of an investigation, proceeding, hearing, or action, including an investigation conducted by the employer, or (c) consistent with the contractor’s legal duty to furnish information.