Responsibilities: Identify complex business problems and work towards building analytical solutions in-order to create large business impact. Demonstrate leadership through innovation in software and data products from ideation/conception through design, development and ongoing enhancement, leveraging user research techniques, traditional data tools, and techniques from the data science toolkit such as predictive modelling, NLP, statistical analysis, vector space modelling, machine learning etc. Collaborate and ideate with cross-functional teams to identify strategic questions for the business that can be solved and champion the effectiveness of utilizing data, analytics, and insights to shape business. Contribute to company growth efforts, increasing revenue and supporting other key business outcomes using analytics techniques. Focus on driving operational efficiencies by use of data and analytics to impact cost and employee efficiency. Baseline current analytics capability, ensure optimum utilization and continued advancement to stay abridge with industry developments. Establish self as a strategic partner with stakeholders, focused on full innovation system and fully supportive of initiatives from early stages to activation. Review stakeholder objectives and team's recommendations to ensure alignment and understanding. Drive analytics thought leadership and effectively contributes towards transformational initiatives. Ensure accuracy of data and deliverables of reporting employees with comprehensive policies and processes.
As the Lead Engineer - Deep Learning, you will be responsible for leading research, software implementation for new concept prototypes in the areas of computer vision and deep learning. What you will do: Focusing on developing new concepts and user experiences through rapid prototyping and collaboration with the best-in-class research and development team. Reading research papers and implementing state-of-the-art techniques for computer vision Building and managing datasets. Providing Rapid experimentation, analysis, and deployment of machine/deep learning models Based on requirements set by the team, helping develop new and rapid prototypes Developing end to end products for problems related to agritech and other use cases Leading the deep learning team Required Candidate profile What you need to have: MS/ME/PhD in Computer Science, Computer Engineering equivalent Proficient in Python and C++, CUDA a plus International conference papers/Patents, Algorithm design, deep learning development, programming (Python, C/C++) Knowledge of multiple deep-learning frameworks, such as Caffe, TensorFlow, Theano, Torch/PyTorch Problem Solving: Deep learning development Vision, perception, control, planning algorithm development Track record of excellence in the machine learning / perception / control, including patents, publications to international conferences or journals. Communications: Good communication skills
Understanding business objectives and developing models that help to achieve them,along with metrics to track their progressManaging available resources such as hardware, data, and personnel so that deadlinesare metAnalysing the ML algorithms that could be used to solve a given problem and rankingthem by their success probabilityExploring and visualizing data to gain an understanding of it, then identifyingdifferences in data distribution that could affect performance when deploying the modelin the real worldVerifying data quality, and/or ensuring it via data cleaningSupervising the data acquisition process if more data is neededDefining validation strategiesDefining the pre-processing or feature engineering to be done on a given datasetDefining data augmentation pipelinesTraining models and tuning their hyper parametersAnalysing the errors of the model and designing strategies to overcome themDeploying models to production
Experience: Minimum 4 years of experience or Master’s Degree in Data Science industry Experience of leading data analytics team would be preferred. Responsibilities The role requires deep knowledge in text mining, data wrangling, testing and deploying analytics solutions including the following: Natural Language Processing (NLP), Working with different data sources, Text Clustering, Statistical Modelling, Topic Modelling, Information Extraction, Information Retrieval, API’s & Web Scraping, Hypothesis Testing, cognitive science, and analytics. Excellent understanding of Analytics concepts and methodologies including machine learning (unsupervised and supervised). The measure, interpret and derive learning from results of the analysis that will lead to improvements in document processing. Team Management - Data Analysis Skills and Qualifications Python, NLP, NLG Text Mining Implemented Data Pipelines using Tensorflow/Pytorch Hands on experience on managing unstructured and structured data Machine Learning, Deep Learning & Neural Networks(Knowledge) Unsupervised & Supervised Learning GPU Computing Good knowledge of statistics/probability/Linear Algebra/Vector Calculus Frameworks: Keras/Tensorflow/PyTorch
Job Description We are looking for a data scientist that will help us to discover the information hidden in vast amounts of data, and help us make smarter decisions to deliver even better products. Your primary focus will be in applying data mining techniques, doing statistical analysis, and building high quality prediction systems integrated with our products. Responsibilities Selecting features, building and optimizing classifiers using machine learning techniques Data mining using state-of-the-art methods Extending company’s data with third party sources of information when needed 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 Skills and Qualifications Excellent understanding of machine learning techniques and algorithms, such as Linear regression, SVM, Decision Forests, LSTM, CNN etc. Experience with Deep Learning preferred. Experience with common data science toolkits, such as R, NumPy, MatLab, etc. Excellence in at least one of these is highly desirable Great communication skills Proficiency in using query languages such as SQL, Hive, Pig Good applied statistics skills, such as statistical testing, regression, etc. Good scripting and programming skills Data-oriented personality
We are looking for Smart Deep learning Computer Vision Data Scientist. Phd person is preferable.
Company Profile : Precily AI is a startup headquartered in Delhi NCR. Precily is currently working with leading consulting & law firms, research firms & technology companies. Aura (Precily AI) is a data- analysis platform for enterprises that increase the efficiency of the workforce by providing AI- based solutions. Responsibilities & Skills Required : The role requires deep knowledge in designing, planning, testing and deploying analytics solutions including the following : 1) Natural Language Processing (NLP), Neural Networks, Text Clustering, Topic Modelling, Information Extraction, Information Retrieval, Deep learning, Machine learning, cognitive science, and analytics. 2) Proven experience implementing and deploying advanced AI solutions using R/Python. 3) Apply machine learning algorithms, statistical data analysis, text clustering, summarization, extracting insights from multiple data points. 4) Excellent understanding of Analytics concepts and methodologies including machine learning (unsupervised and supervised). 5) Hand on in handling large amounts of structured and unstructured data. 6) The measure, interpret and derive learning from results of the analysis that will lead to improvements document processing. Skills Required : 1) Python, R, NLP, NLG, Machine Learning, Deep Learning & Neural Networks 2) Word Vectorizers 3) Word Embeddings ( word2vec & GloVe ) 4) RNN ( CNN vs RNN ) 5) LSTM & GRU ( LSTM vs GRU ) 6) Pretrained Embeddings ( Implementation in RNN ) 7) Unsupervised Learning 8) Supervised Learning 9) Deep Neural Networks 10) Framework: Keras/Tensor flow 11) Keras Embedding Layer output
•Analytics, Big Data, Machine Learning (including deep learning methods): Algorithm design, analysis and development and performance improvement o Strong understanding of statistical and predictive modeling concepts, machine-learning approaches, clustering, classification, regression techniques, and recommendation (collaborative filtering) algorithms Share CV to me at
• Using statistical and machine learning techniques to analyse large-scale user data, including text data and chat logs; • Applying machine learning techniques for text mining and information extraction based on structured, semi-structured and unstructured data; • Contributing to services like chatbots, voice portals and dialogue systems • Input your own ideas to improve existing processes on services and products
JOB DESCRIPTION We're looking for Head, Machine learning (3+ years experience) for our company - Spotmentor Technologies. Right now our Technology team has 5 members and this is a head team member role and carries significant equity with it. We need someone who can lead the Machine learning function with both vision and hands-on work and is excited to use this area to develop B2B products for enterprise productivity. RESPONSIBILITIES • Collaborate with cross-functional team members to develop software libraries, tools, and methodologies as critical components of our computation platforms. • Also responsible for software profiling, performance tuning and analysis, and other general software engineering tasks. • Use independent judgment to take existing code, understand its function, and change/enhance as needed. • Work as a team leader rather than a member. REQUIREMENTS • Proficient in Python with sound knowledge in the machine learning libraries namely Scikit-learn, Numpy, Pandas, NLTK etc. • Experience with Deep Learning tools like TensorFlow, Keras, PyTorch etc and integrating using open source learning platforms is required. • Prior experience in building a fully functional Machine Learning Algorithm in the text analysis and multi-class classification with promising results. • Expert data scientist with professionalism in text classification, text analytics, regression and other machine learning algorithms. • Solid grasp of mathematical principles behind machine learning algorithms. • Proficient in using version control tools (Git, Mercurial etc). • Prior experience of using big data technologies like Hadoop, Spark etc. • Semantic Web experience is a big plus. • Should be from tier 1 colleges (IIT’s / NIT’s and BITS).
Artificial Intelligence Architect (Leader Level – 3-7yrs) Who is Mastercard? We are the global technology company behind the world’s fastest payments processing network. We are a vehicle for commerce, a connection to financial systems for the previously excluded, a technology innovation lab, and the home of Priceless®. We ensure every employee has the opportunity to be a part of something bigger and to change lives. We believe as our company grows, so should you. We believe in connecting everyone to endless, priceless possibilities. Overview Services and Data is helping to shape the future of data insights by leveraging billions of anonymized, aggregated transactions in a 10 petabyte date warehouse to help financial institutions, merchants, media, and governments manage their businesses more effectively. Advanced Analytics is charged with servicing clients by centralizing and optimizing the world class analytical, modeling, software coding, data sourcing, product development, product management, econometrics, and associated delivery capabilities of the MasterCard Advisors organization. It focuses on creating innovative technology solutions which leverage technology in the data science, artificial intelligence and analytics arenas; on enabling the field with industrialized, repeatable products; and on researching and incubating emerging technology to determine how they might apply to our customers and facilitate positive outcomes. • Are you motivated by developing new Analytical skills, leading to insights into issues and developing recommendations that add real value to clients? • Have you managed the client independently and effectively? • Do you want to play a key role in driving a world beyond cash? Role 1. Leading the formulation of artificial intelligence and machine learning solution objectives. Working on technical requirements based on user need. 2. Responsible for creating the framework of solutions that take data intensive and complex business challenges and provide easily consumed and automated outcomes 3. Using unique visualization techniques, condenses large volumes of complex ideas into elegant and simple visual models 4. Determine opportunities to exploit new data sources or leverage new outcomes from existing data sources, by applying new models and algorithms to create business value 5. Influences a client's strategic decisions by using deep industry expertise and deploying innovative Deep Learning analytics solutions in the operational systems Leadership Skills 1. Works closely with clients/internal stakeholders to understand their business needs and design a technical solution 2. Proving viability of the solution through mechanisms like proof-of-concepts 3. Thought Leadership in AI – Developing compelling audience-specific messages and tools. Capture and share best practices and insights internally and with partners and customers. 4. Self-driven, energetic, creative with ability to work in global teams. Excellent communication skills and a wide knowledge base, to convince the Stakeholders on application of AI/ML 5. Provide technical leadership to the team members in relevant topics on AI/ML 6. Adaptable - Takes in stride and constantly attunes to the changing needs of a highly dynamic business 7. Curious - our key contributors are always seeking to grow their knowledge, to gain new perspectives, and to find better ways forward by researching and showcasing latest AI tools, techniques and applications 8. Tenacious - self-starters who will take ownership of projects and bring them to completion despite difficulties or setbacks Functional and Technical Skills 1. Experience in building and implementing AI application in any of the 2 domains - Fraud, risk, marketing, finance, operations for Banks, Fintech, Ecommerce, merchants or retailers 2. Has a deep understanding of business value industry requirements and advanced analytic models (statistical, operations research, computing process) 3. Responsible for building applications using artificial intelligence/machine learning technology, applying latest industrial and academic developments, prefer Kaggle hackathon participant 4. Expertise in deep learning frameworks for Production Systems like Tensorflow, Keras (for RPD and neural net architecture evaluation), PyTorch and Xgboost, Caffe, Theono 5. Proficient in Deep Learning algorithm techniques like Random Forest, GBM, KNN, SVM, Bayesian, Text Mining techniques, Multilayer Perceptron, Neural Networks – Feedforward, CNN, LSTM’s GRU’s. Optimization techniques – Activity regularization (L1 and L2), Adam, Adagrad, Adadelta concepts; Cost Functions in Neural Nets – Contrastive Loss, Hinge Loss, Binary Cross entropy, Categorical Cross entropy; developed applications in KRR, NLP, Speech and Image processing 6. Expertise in classical Machine Learning algorithms like Logistic Regression, Decision trees, Clustering (K-means, Hierarchical and Self-organizing Maps), TSNE, PCA, Bayesian models, Time Series ARIMA/ARMA, Recommender Systems - Collaborative Filtering, FPMC, FISM, Fossil etc 7. Extensive experience in statistical tools and programming environments like Python, R, SAS, SQL 8. Experience with BIG DATA platforms - Hadoop, Hive, Spark, GPU Clusters for deep learning 9. Consulting and Project Management Experience preferred 10. Minimum Graduate degree in Mathematics/Computer Science/Engineering
We are looking for Freelance online Data Scientist Trainers who can work with us part - time with the following skills: Experience using statistical computer languages (R, Python etc.) to manipulate data and draw insights from large data sets. Should have strong programming & Good applied statistics skills, such as distributions, statistical testing, regression, etc. Knowledge of a variety of machine learning techniques (clustering, decision tree learning, artificial neural networks, etc.) and their real-world advantages/drawbacks.Few examples are k-NN, Naive Bayes, SVM, Decision Forests,Random Forest ,Support vector machine,Principal component analysis. Experience using natural language processing techniques and Deep Learning using TensorFlow will be a plus. Should have minimum 5 years of relevant work experience.