- Develop math models and algorithms and deploy models created with large social media training data sets.
- Evaluating state-of-the-art statistical modeling and Machine Learning approaches.
- Develop unique approaches to complex modeling and inference problems which combine marketing and social media data knowledge with mathematical approaches to recognize patterns and trends in influencer marketing industries.
- Data Analysis, Visualization and Modeling with large text, image and video datasets
- Create data pipelines and demonstrate processes for Data Acquisition, Ingestion, Cleaning, and Transformation
- Work closely with the CTO, Product, Engineering, DevOps and Ai Science teams
- Collaborate with the product team, share feedback from project implementations and influence the product roadmap.
- Be comfortable in a highly dynamic, agile environment without sacrificing the quality of work products.
Stay current with emerging AI, MLOps, web and mobile technologies and trends.
- Diploma or Degree in Computer Science or related field. Advanced degree (MA/MSc, equivalent or higher) is preferred.
- 3+ years’ experience as a Data Scientist and/or Python Engineer
- Strong ability to apply various analytical models to business use cases (NLP, Supervised, Un-Supervised, Neural Nets, etc.)
- Strong proficiency with Machine Learning concepts and modeling techniques to solve problems such as clustering, classification, regression, anomaly detection, simulation and optimization problems, and other statistical analytical techniques, data mining, and predictive modeling on large scale data sets.
- Experience with SQL and NoSQL databases
- Knowledge of ML automation tools such as DataRobot and/or H20 or AWS/Azure cloud services.
- Strong knowledge of Python ML tools, including scikit-learn, and Pandas or similar frameworks, and different deep neural networks architectures (RNN, CNN, GAN, seq2seq/Transformers) using Could ML (AWS, GCP, Azure), or similar tools
- Strong experience with Machine Learning agile project management and experience implementing ML best practices for the entire Data Science lifecycle
- Database and programming languages experience and data manipulation and integration skills using (one or more) SQL, Oracle, Hadoop, NoSQL and Graph Databases, or similar tools is required
- Experience with data visualization tools — Tableau etc. preferred
- Experience with AWS services EC2, ECS, serverless computing, EBS, RDS, S3, IAM
- Knowledge of CI/CD tools and processes; Git, Jenkins, Docker, CircleCI
- Knowledge of DevOps/MLOps and creating data pipeline on AWS environments.