Sutherland Global Services is seeking Data Scientist (Machine Learninig) with experience in building high-performing, scalable, enterprise-grade applications. You will be part of a talented software team that works on mission-critical applications. As a Data scientist you should be Proficient in SQL and experience with efficient processing of large data sets. Ability to write sophisticated and optimized queries against large databases. Proficient in data visualization tools such as Mode Analytics or Tableau, Proficient in Excel, Experience in statistical computing with Python/R. Ability to handle several concurrent activities with strong organizational skills and attention to detail.
To succeed in this position, you must have 5 – 8 years of experience in architecting enterprise applications using the below technologies
- Deep knowledge of math, probability, statistics and algorithms to help strategize and guide developers to build algorithms.
- Excellent understanding of machine learning techniques and algorithms, such as k-NN, Naive Bayes, SVM, Decision Forests, etc.
- Experiences with one or more of the following is highly desirable: HPC/Parallelization, operationalizing ML models; cloud computing (e.g. Google Cloud, AWS, etc.); familiarity with ML frameworks such as TensorFlow, Theano, MXNet and ML-JS, etc
- Good experience in a few of the following areas: deep neural networks, reinforcement learning, Markov Random Fields, Bayesian networks, semi-supervised learning, computer vision, image processing, signal processing, distributed computing, and/or numerical optimization
- 2+ years of experience with computer vision and deep learning solutions, including image classification, object detection, segmentation, and equivalent computer vision-based vision tasks
- Experience with common data science toolkits, such as R, Weka, NumPy, etc . Excellence in at least one of these is highly desirable
- Proficiency in using query languages such as SQL, Hive, Pig
- Good applied statistics skills, such as distributions, statistical testing, regression, etc.
- Good scripting and programming skills
To succeed in this position, you must:
- Selecting features, building and optimizing classifiers using machine learning techniques
- Data mining using state-of-the-art method
- Enhancing data collection procedures to include information that is relevant for building analytic systems
- Processing, cleansing, and verifying the integrity of data used for analysis
- Doing ad-hoc analysis and presenting results in a clear manner
- Creating automated anomaly detection systems and constant tracking of its performance