This role requires the data scientist to be a hands-on software developer and interested in solving real world physics-based problems.The work will involve management and problem analysis, data exploration and preparation, data collection and integration, AI/machine learning modelling, and operationalization.
Responsibilities:
Work on a team designing innovative software and algorithms to solve real-world problems in industrial processes.
Use machine learning techniques, statistical tests, classical algorithms, and physically-motivated models as appropriate
Perform in-depth exploratory analysis of data from the field and laboratory experiments
Acquire domain knowledge about mechanical systems to aid in analysis and interpretation. Display drive and curiosity to understand the business process to its core
Understand new data sources and process pipelines, and catalog/document them
Work with various databases such as SQL or graph databases, and other source systems
Apply a variety AI/ML and advanced analytics techniques to perform classification or prediction tasks
Run experiments and test models using cross-validation, A/B testing, bias and fairness, etc.
Maintain and update the analytics software platform (Python)
(Help to) create data pipelines for more efficient and repeatable data science projects
Establish best practices around AI/ML production infrastructure
Condition Monitoring, Time series Analytics
Upskill yourself (through conferences, publications, courses, local academia and meetups)
Qualifications
A bachelor s or master s degree in computer science, data science, operations research, statistics, applied mathematics, or a related quantitative field such as, economics, engineering and physics is preferred. Experience in more than one area is strongly preferred
Relevant experience with 5+ years
3+ years of relevant project experience in successfully launching, planning, executing of data science projects. Preferably in the domains of imaging systems, risk modelling, customer behavior prediction, quality assessment, and/or factory automations
Knowledge and experience in statistical and data mining techniques: generalized linear model (GLM)/regression, random forest, boosting, trees, text mining, hierarchical clustering, deep learning, convolutional neural network (CNN), recurrent neural network (RNN), T-distributed Stochastic Neighbor Embedding (t-SNE), graph analysis, etc
Strong knowledge of math, probability, statistics, and algorithms
Ability to write robust, production-ready Python code
Adept in agile development methodologies
Self-driven, curious and creative
They must demonstrate the ability to work in diverse, cross-functional teams in a dynamic business environment
Should be confident, energetic self-starters. Ability to make progress on large projects independently or as part of a team