Senior Data Scientist @ Locus It Services

• Contributing in development and deployment of applied, predictive and prescriptive analytics. Develop self-learning systems that can predict failures and autocorrect based on data

• Gathering analysing data, devising data science solutions for high-performance models in scalable code. Propose innovative algorithms and pursue patents where appropriate.

• Working with engineering teams to incorporate solutions and create intuitive UX stories. Partner with data engineers on data quality assessment, cleansing and analytics.

• Researching and evaluating emerging technology and market trends to assist in project development and operational support for multiple teams or complex scenarios.

• Contributing to the development of software and data delivery platforms that are service-oriented with reusable components across multiple teams.

• Creating reports and other artifacts to document your work and outcomes. Communicating methods, findings, and hypotheses with stakeholders.

To be successful in this role you will:

• Have a MS Degree in Computer Science or in STEM, Majors. 6+ years as data scientist and technical hands-on coding experience.

• Have experience in Machine Learning/AI techniques including Deep learning (RNN, CNN, GAN, etc), Support Vector Machines; Regularization Techniques; Boosting, Random Forests, Ensemble Methods, image/video/audio processing, Bayesian and time series modelling.

• Have experience in Parallel programming frameworks for GPUs, TPUs and developed containerized solutions (Docker/Mesos etc).

• Have good implementation experience with R, Python, Perl, Ruby, Scala, Apache Spark, Storm, SAS and ability to work with a variety of Deep learning frameworks including TensorFlow, Keras, Caffe, CNTK, etc

• Have hands-on skills in sourcing, manipulating and analysing large volumes of data including SQL and NoSQL databases

• Have proven experience in using well-established supervised and unsupervised machine learning methods for large industry-strength data analysis problems.

• Reviews, analyses and develops architectural requirements at domain level, aligning architectural requirements with software development strategy. Leads and facilitates the domains architecture governance process based on EAs governance structure.