Deep Learning

MS or higher in the following areas: Statistics and Mathematics
At least 3-5 years of professional industry experience, in addition to your academic experience
Outstanding quantitative analytical ability
Able to take less than precise business requirements and translate them into logic problems which you enjoy solving
Independent and creative approach to problem solving
Excellent written and verbal communication skills, with prior experience explaining assumptions, conclusions and methodology to both internal and external customers
In-depth knowledge of Statistics/Probability/Machine Learning
General Statistical concepts such as hypothesis testing, estimation, inference
Supervised and unsupervised statistical techniques such as regression (linear / logistic), time series analysis, clustering
Machine Learning foundations such as bias/variance trade-off, regularization, dimension reduction
Real world experience with popular machine Learning algorithms such as Random Forest, Boosting, SVMs
Experience with unstructured text data using NLP methods such as Latent Dirichlet Allocation (LDA), Latent Semantic Analysis (LSA), Sentiment Models, Word Embeddings, Text Similarity, Entity extraction is a strong plus
Strong programming experience in Pyton and one of the following: Scala/Java, R
Understanding of algorithm complexity and performance implications
Knowledge of data structures and algorithms
Good knowledge of Knowledge Engineering
Good knowledge of Graph technology, Knowledge Graphs, Graph Data bases and Ontologies
Experience with SQL
Familiarity with R Shiny framework is a plus

— Technologies:
Mediapipe framework (Skeleton models and behavior recognition)
yolov5
CNNs
Torch
Python
OpenCV
Experience with models on edge devices

— Experience:
2+ years in CV applications;
previous experience in building high load computer vision applications;
previous experience in training and deploying of light models on EDGE device;
models distillation.

• Experience in commercial development in Rust
• Experience in participating in open-source projects
• Experience in managing a development team
• Strong English communication and presentation skills

● Python experience
● Prior experience with machine learning or computer vision is a big plus, but may not be strictly necessary if you are able to learn fast and understand relevant technical documentation.

-3+ years (middle) and 5+ years (senior) of successful data science experience
-Experience with/good knowledge of NLP, pattern recognition, deep learning and other ML technologies
-Excellent programming skills, expert in Python
 - Strong experience with Tensorflow / PyTorch / Keras
-Degree in Math / Statistics / Physics

• Preferred: experience with big data processing technology stack (Spark, Hadoop, etc.)
  -A sense of urgency and ownership over the product
  -Proactive, high-energy, competitive, and creative individual
  -Driven to outperform; dedicated to self-improvement
  -Experience in a remote SCRUM team involving daily meetings, grooming, planning, etc
  -English level: Intermediate or higher

• hands-on experience with developing, evaluating, and releasing computer vision algorithms into production software;
• 2+ years of experience in developing novel computer vision algorithms or similar methods based on deep learning in such topics as image recognition, geometric deep learning, object recognition using 3D spatial data
• passionate about what you do and enjoy working in a collaborative environment
• upper-intermediate+ verbal and written English level.

• A minimum of a Bachelor’s degree in a mathematical discipline such as Computer Science, Applied Statistics, Maths, Engineering, or Physics from a respected University. A Ph.D. is a bonus
• 4+ years experience in Python and good knowledge of R
• At least 4 years practical experience of univariate and multivariate statistical analysis in Python or R with large data sets (millions of records and many tens or hundreds of independent variables)
• Good experience of variable transformation and data preprocessing techniques to extract maximum predictive power such as binning, piecewise linear regression, non-linear function transforms, etc.
• Excellent practical knowledge of multi-variate techniques such as Logistic Regression, Decision Trees, Random Forest, Naive Bayes, Clustering, etc. and a good grasp of the strengths and weaknesses of specific approaches
• Strong communication skills

Ability to write code in Python.
Strong algorithmic skills.
Basic knowledge and practical experience of Machine Learning.
Knowledge of Machine Learning-related math (linear algebra, probability theory, etc.).
Basic knowledge of software architecture design.
Active involvement and interest in Machine Learning.
Experience with some Machine Learning-related libraries, such as NumPy, Matplotlib, NLTK, Pandas, SkiPy and others.
Communication skills.

● Hands-on experience (3-6 years) developing machine learning solutions with Python for Unstructured Text Based Problems (NLP), including: ML problem analysis, Data preparation (Exploratory Data Analysis) & transformation, Feature analysis & selection, Topic Modelling & NER (Named Entity Recognition), ML model selection using parameter tuning, Model result analysis and presentation, Transfer Learning, Developing custom ML Model based on requirement
● Good knowledge of machine learning libraries (like Scikit-learn, NLTK, spacy etc.)
● Strong programming knowledge in Python, Flask, NumPy, Pandas etc.
● Good knowledge in statistics and algorithms
● Experience in image recognition or OCR technology