Data Science Software Engineer
TheEasyData is an innovative "Data-Engineering-as-a-Service" business with a vision to democratise data engineering.
We are an Intel incubator graduated venture founded by a group of passionate individuals with expertise in data science, AI/ML algorithmic development and cloud development. We foster a collaborative, supportive, and exciting environment where the brightest minds come together to achieve exceptional results.
We see challenges as a tremendous opportunity and focused on execution. We are determined to expand our proprietary cognitive AI core technology and develop disruptive solutions that will shape the future of data engineering and data centric AI landscaping. We are looking for exceptionally smart people with a can-do attitude and believe that data will be the future of AI that will change the world.
Roles and Responsibilities
Define data engineering functionalities, including data ingestion, data preparation, data transformation, data exploration, data analysis, insight generation etc.
Create, develop, and test data engineering workflow and transform it into code and/or APIs that other applications can interact with.
Functional lead who guides and mentor junior engineers and contingent workers to ensure quality and bug-free deliverables.
Direct technical interaction with tech partners and customers with a high level of autonomy to define solutions, training, and inference execution to reach customer desired targets of work efficiency and accuracy.
Anticipate market and industry trends as well as customer needs for scaling on advance data science functionalities, data analytic and insight generation, DataOps solutions etc.
Research technological trends and advancements, create proof of concepts, or prototype new ideas, recommend strategic product enhancements to ensure product relevancy and support revenue growth.
Experience and Skills
Bachelor's, master's degree or PhD in Data Science, AI/ML, Computer Science, Computer Engineering, or Mathematics/Statistics or other technical discipline.
Good scripting and programming skills. Proficient in Python, Rust, Java, bash scripting, and other programming skills would be a plus, including C/C++, Java, Matlab, R, Go.
Hands-on experience with end-to-end data engineering workflow from data ingestion, data cleaning, data analytic, data modelling, evaluation, and deployment.
Experience with common data science toolkits, visualisation tools and SQL/NoSQL databases.
Strong applied statistical skills, including knowledge of statistical tests, distributions, regression, maximum likelihood estimators, etc.
Excellent understanding of machine learning methods like k-Nearest Neighbors, Naive Bayes, SVM, Decision Forests, etc. Experience using ML frameworks such as TensorFlow, PyTorch, or scikit-learn.
Strong ownership attitude and problem-solving skills with results orientation.
Excellent communication skills to both technical and non-technical audiences, including higher management, sales and marketing team, internal and external customers.