Data Scientist Job in Dataeaze

Roles and responsibilities

• Understand business requirements and formulate solution for the problem, satisfying the time, memory and accuracy SLAs.
• Research techniques in computer science and mathematics for elegant solution.
• Code, train, evaluate and deploy models that integrate with the complete software solution.
• Own and deliver the product to the end-user, in the time allotted for the project.

Qualifications

• M Sc (Mathematics / Statistics) or M Tech (Computer Science) with specialization in machine learning is required
• Ph D is preferable
• Prerequisites mentioned below
• Work-experience is preferable, but we are looking for expertise rather experience in number of years

Skills

• Candidate must be good at programming and be able to adapt to any of the basic programming languages like C, C++, Python, Matlab, R, Julia, Java, Go, Rust etc.
• Candidate must have mastery of basic computer science concepts like data structures, algorithms, databases, relational algebra (SQL), operating systems, computer architecture, computer networks.
• Candidate must be comfortable in programming on GNU/Linux in a high performance computing (HPC) setups like multicores, clusters, GPUs, etc.
• Candidate must be able to grasp concepts from latest research papers and implement them in a short time
• Candidate must have mastery over basics of machine learning and hands on experience with recent advances in deep learning
• Candidate must have a specialization in AI / ML and should have mastery over few of the topics in the prerequisites section
• Candidate must be familiar with ML programming frameworks and libraries and should be able to quickly learn and adapt to the newly emerging ones

Pre-requisites

Techniques

• Linear Algebra | Prof. Gilbert Strang
• Computational and Inferential Thinking: The Foundations of Data Science | Ani Adhikari, John DeNero, David Wagner
• Bayesian Data Analysis | Gelman, Carlin, Stern, Dunson, Vehtari, Rubin
• Introducing Monte Carlo Methods with R | Robert, Christian, Casella, George
• Probabilistic Programming and Bayesian Methods for Hackers | Cam Davidson Pilon
• Numerical Optimization | Nocedal and Wright
• Practical Methods of Optimization | R Fletcher
• Numerical Recipes, The Art of Scientific Computing | William H. Press, Saul A. Teukolsky, William T. Vetterling and Brian P. Flannery
• Bayesian Reasoning and Machine Learning | David Barber
• Probabilistic Graphical Models
• Causal Inference in Statistics : A Primer | Judea Pearl, Madelyn Glymour, Nicholas P. Jewell
• Causal Inference: What If | Hern n MA, Robins JM
• Elements of Causal Inference Foundations and Learning Algorithms| Bernhard Sch lkopf, Dominik Janzing, and Jonas Peters
• Geometric Deep Learning | Michael Bronstein
• Graphical Models, Exponential Families and Variational Inference | Wainwright, Prof Michael I Jordan
• Variational Inference: A Review for Statisticians | David M. Blei, Alp Kucukelbir, Jon D. McAuliffe
• Information Theory, Inference and Learning Algorithms | David J.C. MacKay
• Bayesian Learning | Shakir Mohammed
• Bayesian Deep Learning and Probabilistic Model Construction | Andrew Gordon Wilson
• Statistical Learning Theory | Prof. Vladimir Vapnik
• Gaussian Processes in Machine Learning |
• Practical Time Series Forecasting | Galit Shmueli, Kenneth C. Lichtendahl Jr
• Advances in Kernel Methods Support Vector Learning | Christopher J.C. Burges, Bernhard Sch lkopf and Alexander J. Smola
• Semi-Supervised Learning | Olivier Chapelle, Bernhard Scholkopf, Alexander Zien
• The Algorithmic Foundations of Differential Privacy | Cynthia Dwork, Aaron Roth
• Privacy Preserving Data Science Explained | OpenMined
• Privacy-Preserving Machine Learning | J. Morris Chang, Di Zhuang, and G. Dumindu Samaraweera

Tools

In addition to the tools mentioned in Machine Learning Engineer section, candidate should be comfortable with specilised statistical modeling tools including