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Expertise in the development of forecasting models (machine learning and stochastic predictive models) In-depth knowledge and skills to analyze large datasets using both statistical and advanced data mining/machine learning algorithms Analysis of large datasets from Plugged-in Midlands Project (PiM) and eBridge project related to Electric Vehicles charging events and EV trips (R studio, Matlab, SPSS, WEKA tool and RapidMiner software packages used) Cluster Analysis using k-means algorithm to extract typical profiles (Matlab, R) Statistical Analysis using Regression models to investigate the long-term trend of a time series (Matlab, SPSS) Development of a quantitative risk analysis (decision making) model (based on Fuzzy-Logic) to identify the potential risk of EV charging demand on a distribution network (Matlab) Spatial Data Analysis (using Google Earth/Google Maps, Open Street Maps) Data Visualization experience (UML diagrams, Matlab, Microsoft Office) Professional experience using tools and techniques to utilize Business Intelligence in order to drive competitive advantages and create new strategic business opportunities Organizational and Analytical skills, Strong commitment to learn and a strong drive to achieve results, Intellectual skills and creative thinking, Ability to work independently and within a team environment and deal in a challenging environment under pressure, Strong independent decision-making, organizational, planning and problem-solving skills
MSc in Data Science, Distinction from Lancaster University, UK • 6+ years of experience as Systems Analyst/Solution Designer in Banking projects. Solid understanding of statistical methods and Machine Learning with hands on experience in Model building and validations (Reinforcement learning, Regression, Deep Learning, Convoluted Neural Network, Decision trees, GBM, Mixture Density models.) Delivered customer churn prediction model for Square Inc, US (Neural Network and Logistic regression) Implemented TD-Sarsa with Stochastic Gradient Descent and mean square error correction manually in Java. 3+ years of work experience in agile environment.
Implementation of Map Reduce and study of various large-scale Machine learning frameworks
I am fluent in Machine Learning, Deep Learning, Computer Vision, Monte Carlo Method, Neural Networks in all its form and Quantum Mechanics. I code must of the time in Matlab and Phyton. I like to solve complex problems in the domain of Machine Learning applied to multiple aspects of life
In the computer vision arena, I have the full knowledge of all the aspects. From Convolutional Neural Networks to the most complex image segmentation technologies, therefore, I am able to deliver very complex projects on time.
In Machine Learning and Deep Learning, I use the most advanced technologies from Adversarial Networks to Neural Differential Equations. I use other more standard solutions in other to deliver on time what is requested
I create and patent a model that analyze and predict the human decision-making process applying Quantum Mechanics mixes with Machine Learning and Computer Vision. From retail to politics to online activity, to what a client will buy etc. Anything your client does that has a human decision involved I will give you a higher degree of certainty about the outcome than a standard solution does. My product capture the bias, irrationalities, lack of information
Professional with long experience in IT, working in different domain knowledge in Banking and Defense sectors. Across these years I was involved in different disciplines covering not only software development, IT Infrastructure or Security but also pure business and operational topics. I have 15+ years of international experience in finance sector working for some of the most renowned names on this industry and the ability to understand business needs, regulatory requirements, and manage disperse and multicultural teams. Experience in developing productive tools using Machine Learning, Deep Learning (AI) and Reinforcement Learning models and working with Time series and Natural Language Processing techniques in Finance Industry Experience in Technical and fundamental financial analysis in combination with Sentimental analysis. Proficiency in SDLC, SQL, ETL and reporting tools Big Data (Hadoop, Hortonworks, MapR, Hive, Pig, MapReduce, Hbase, Spark), Data-Mining and Analytics. Cloud (AWS and Azure)
Machine learning is an application of artificial intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed. Machine learning focuses on the development of computer programs that can access data and use it learn for themselves
What does a Machine Learning Engineer do?
They run machine learning experiments using a programming language with machine learning libraries. They also deploy machine learning solutions into production, and optimize solutions for performance and scalability.
What is the average day rate for Machine Learning Engineers?
The average day rate ranges from £570-650 depending on experience.
What should a Machine Learning Engineer know?
Generally, machine learning engineers must be skilled in computer science and programming, mathematics and statistics, data science, deep learning, and problem solving.
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