Minimal qualifications include:
A bachelors degree in Computer Science or equivalent
3+ years of complex software engineering experience in progressively challenging roles including proven skill sets in server and client side programming and best practices
(Optional) Certifications in Machine Learning, Data Analytics, Big Data, Python
3+ years of experience of working in ML domain.
Sound knowledge of machine learning concepts viz. Supervised, Unsupervised ML, train/test data set
Good knowledge and hands on experience of design and development of ML workflow
Experience solving analytical problems using statistical algorithms like Multiple Regression, Clustering techniques, Logistic Regression, Decision Trees, Random Forest
Knowledge of probability (conditional probability, Bayes rule, likelihood, independence, etc.) and techniques derived from it (Bayes Nets, Markov Decision Processes, Hidden Markov Models, etc.)
Experience with common data science toolkits, such as R, Weka, NumPy, MatLab, etc. Excellence in at least one of these is highly desirable.
Hands on experience in developing and maintaining high-volume ETL processes is a plus
Hands on experience in leveraging data, advanced analytics, actionable insights, and guide strategic decisions
Strong verbal, written, and interpersonal communication skills with both technical and non-technical audiences.
Insatiably curious, and constantly learning and experimenting with new technologies
Applicant should be comfortable working independently, and in a team environment, as required
Strong analytical and problem solving skills
Ability to prioritize and manage work to critical project timelines in a fast-paced environment
Perform development and support of existing and new products.
Understand product vision and business needs to develop product requirements
Agile working experience and adhere to process
Strong sense of ownership and accountability
Excellent interpersonal, written and oral communication skills.
Ability to collaborate effectively with interdisciplinary teams, management and customers
Selecting features, building and optimizing classifiers using machine learning techniques
Enhancing data collection procedures to include information that is relevant for building analytic systems
Doing ad-hoc analysis and presenting results in a clear manner
Creating automated anomaly detection systems and constant tracking of its performance
Processing, cleansing, and verifying the integrity of data used for analysis
Data mining using state-of-the-art methods
Work effectively in a fast paced and dynamic environment
Communicate effectively with all stakeholders
Hands on contribution
Motivated self-starter with a high capacity for rapid learning and meticulous attention to detail.