AI/ML Data Scientist
Qualifications: Bachelor’s degree in statistics, applied mathematics, computer science., 7+ years of data science experience., Proficiency in data mining, mathematics, statistical analysis, AI/ML frameworks., Experience with Python, Go, SQL, NoSQL databases, AWS, or Azure..
- Develop innovative models for data analysis and communicate findings.
- Identify and integrate new datasets for product capabilities.
- Perform analytical experiments, clean data, develop AI/ML algorithms.
- Analyze data trends, Implement models, and create microservices for large datasets.
At Experfy, we rely on powerfully insightful data to power our systems and solutions. We’re seeking an experienced data scientist to deliver that insight to us on a daily basis. Our ideal team member will have the mathematical and statistical expertise you’d expect, but a natural curiosity and creative mind that’s not so easy to find. As you mine, interpret and clean our data, we will rely on you to ask questions, connect the dots, and uncover opportunities that lie hidden within—all with the ultimate goal of realizing the data’s full potential. You will join a team of data specialists, but will “slice and dice” data using your own methods, creating new visions for the future.
Objectives of this Role
- Collaborate with product design and engineering to develop an understanding of needs
- Research and devise innovative statistical models for data analysis
- Communicate findings to all stakeholders
- Enable smarter business processes—and implement analytics for meaningful insights
- Keep current with technology and industry developments
Daily and Monthly Responsibilities
- Work as the lead data strategist, identifying and integrating new datasets that can be leveraged through our product capabilities and work closely with the engineering team to strategize and execute the development of data products
- Execute analytical experiments methodically to help solve various problems and make a true impact across various domains and industries
- Identify relevant data sources and sets to mine for client business needs, and collect large structured and unstructured datasets and variables
- Devise and utilize algorithms and models to mine big data stores, perform data and error analysis to improve models, and clean and validate data for uniformity and accuracy
- Developing AI/ML algorithms to analyze huge volumes of historical data to make predictions and recommendations
- Developing microservices with integrations to large datasets and other sub systems such as Elastic Search
- Analyze data for trends and patterns, and Interpret data with a clear objective in mind
- Implement analytical models into production by collaborating with software developers and machine learning engineers
- Documenting artificial intelligence and machine learning processes
- Communicate analytic solutions to stakeholders and implement improvements as needed to operational systems
Skills and Qualifications
- Bachelor’s degree in statistics, applied mathematics, or related discipline or computer science
- 7+ years experience in data science
- Proficiency with data mining, mathematics, and statistical analysis
- Advanced pattern recognition and predictive modeling experience
- Extensive knowledge of AI/ML frameworks, libraries, data structures, data modeling, and software architecture
- Five plus years of developing models and algorithms independently, writing your own code, developing a strategy for algorithmic experimentation, and deploying in production
- Proficiency with Python and Go programming languages
- Experience with Microservices architecture
- Experience with SQL and NoSQL databases such as MongoDB, Postgres, Neo4j, etc.
- Experience with AWS, Azure, or other distributed compute services
- Comfort working in a dynamic, research-oriented group with several ongoing concurrent projects
- Sharp problem-solving skills and a curious mindset.
- Strong analytical thinking and the ability to uncover hidden opportunities.
- Effective communication and collaboration with stakeholders.
- Adaptability in a fast-paced research-oriented environment.