Zycus is looking for applicants with a strong background in Analytics and Data mining (Web, Social and Big data), Machine Learning, Pattern Recognition, Natural Language Processing, Computational Linguistics, Statistical Modelling, Inferencing, Information Retrieval, Large Scale Distributed Systems, Cloud Computing, Econometrics, Quantitative Marketing, Applied Game Theory, Mechanism Design, Operations Research, Optimization, Human Computer Interaction and Information Visualization. Applicants with a background in other quantitative areas are also encouraged to apply.
We are looking for someone who can create and implement AI solutions. If you have built a product like IBM WATSON in the past and not just used WATSON to build applications, this could be the perfect role for you.
All successful candidates are expected to dive deep into problem areas of Zycus’ interest and invent technology solutions to not only advance the current products, but also to generate new product options that can strategically advantage the organization.
Roles & Responsibilities:
- Act as a technical thought leader in collaboration with the analytics leadership team, helping to set the strategy and standards for Machine Learning and advanced analytics
- Work with senior leaders from all functions to explore opportunities for using advance analytics
- Provide technical leadership, coaching, and mentoring to talented data scientists and analytics professionals
- Guide data scientists in the use of advanced statistical, machine learning, and artificial intelligence methodologies
- Guide the work of other Machine learning team members to provide support and assistance, while also ensuring quality
About Zycus
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Company Overview:
At Codvo, software and people transformations go hand-in-hand. We are a global empathyled technology services company. Product innovation and mature software engineering are part of our core DNA. Respect, Fairness, Growth, Agility, and Inclusiveness are the core values that we aspire to live by each day. We continue to expand our digital strategy, design, architecture, and product management capabilities to offer expertise, outside-the-box thinking, and measurable results.
Required Skills (Technical):
- Advanced knowledge of statistical techniques, NLP, machine learning algorithms and deep learning frameworks like TensorFlow, Theano, Kera’s, Pytorch.
- Proficiency with modern statistical modelling (regression, boosting trees, random forests, etc.), machine learning (text mining, neural network, NLP, etc.), optimization (linear optimization, nonlinear optimization, stochastic optimization, etc.) methodologies.
- Building complex predictive models using ML and DL techniques with production quality code and jointly own complex data science workflows with the Data Engineering team.
- Familiarity with modern data analytics architecture and data engineering technologies (SQL and No-SQL databases).
- Knowledge of REST APIs and Web Services
- Experience with Python, R, sh/bash
Required Skills (Non-Technical):
- Fluent in English Communication (Spoken and verbal)
- Should be a team player
- Should have a learning aptitude
- Detail-oriented, analytically.
- Extremely organized with strong time-management skills
- Problem Solving & Critical Thinking
• Solid technical / data-mining skills and ability to work with large volumes of data; extract
and manipulate large datasets using common tools such as Python and SQL other
programming/scripting languages to translate data into business decisions/results
• Be data-driven and outcome-focused
• Must have good business judgment with demonstrated ability to think creatively and
strategically
• Must be an intuitive, organized analytical thinker, with the ability to perform detailed
analysis
• Takes personal ownership; Self-starter; Ability to drive projects with minimal guidance
and focus on high impact work
• Learns continuously; Seeks out knowledge, ideas and feedback.
• Looks for opportunities to build owns skills, knowledge and expertise.
• Experience with big data and cloud computing viz. Spark, Hadoop (MapReduce, PIG,
HIVE)
• Experience in risk and credit score domains preferred
• Comfortable with ambiguity and frequent context-switching in a fast-paced
environment
- Architecting end-to-end prediction pipelines and managing them
- Scoping projects and mentoring 2-4 people
- Owning parts of the AI and data infrastructure of the organization
- Develop state-of-the-art deep learning/classical models
- Continuously learn new skills and technologies and implement them when relevant
- Contribute to the community through open-source, blogs, etc.
- Take a number of high-quality decisions about infrastructure, pipelines, and internal tooling.
What are we looking for
- Deep understanding of core concepts
- Broader knowledge of different types of problem statements and approaches
- Great hold on Python and the standard library
- Knowledge of industry-standard tools like scikit-learn, TensorFlow/PyTorch, etc.
- Experience with at least one among Computer Vision, Forecasting, NLP, or Recommendation
Systems a must
- A get shit done attitude
- A research mindset and a creative caliber to utilize previous work to your advantage.
- A helping/mentoring first approach towards work
Key deliverables for the Data Science Engineer would be to help us 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 on applying data mining techniques, doing statistical analysis, and building high-quality prediction systems integrated with our products.
What will you do?
- You will be building and deploying ML models to solve specific business problems related to NLP, computer vision, and fraud detection.
- You will be constantly assessing and improving the model using techniques like Transfer learning
- You will identify valuable data sources and automate collection processes along with undertaking pre-processing of structured and unstructured data
- You will own the complete ML pipeline - data gathering/labeling, cleaning, storage, modeling, training/testing, and deployment.
- Assessing the effectiveness and accuracy of new data sources and data gathering techniques.
- Building predictive models and machine-learning algorithms to apply to data sets.
- Coordinate with different functional teams to implement models and monitor outcomes.
- Presenting information using data visualization techniques and proposing solutions and strategies to business challenges
We would love to hear from you if :
- You have 2+ years of experience as a software engineer at a SaaS or technology company
- Demonstrable hands-on programming experience with Python/R Data Science Stack
- Ability to design and implement workflows of Linear and Logistic Regression, Ensemble Models (Random Forest, Boosting) using R/Python
- Familiarity with Big Data Platforms (Databricks, Hadoop, Hive), AWS Services (AWS, Sagemaker, IAM, S3, Lambda Functions, Redshift, Elasticsearch)
- Experience in Probability and Statistics, ability to use ideas of Data Distributions, Hypothesis Testing and other Statistical Tests.
- Demonstrable competency in Data Visualisation using the Python/R Data Science Stack.
- Preferable Experience Experienced in web crawling and data scraping
- Strong experience in NLP. Worked on libraries such as NLTK, Spacy, Pattern, Gensim etc.
- Experience with text mining, pattern matching and fuzzy matching
Why Tartan?
- Brand new Macbook
- Stock Options
- Health Insurance
- Unlimited Sick Leaves
- Passion Fund (Invest in yourself or your passion project)
- Wind Down
The role involves computer vision tasks including development, customization and training of Convolutional Neural Networks (CNNs); application of ML techniques (SVM, regression, clustering etc. ) and traditional Image Processing (OpenCV etc. ). The role is research focused and would involve going through and implementing existing research papers, deep dive of problem analysis, generating new ideas, automating and optimizing key processes.
Requirements:
- 2 - 4 years of relevant experience in solving complex real-world problems at scale via deep learning, computer vision or AI
- Python, cuDNN, Tensorflow/PyTorch/Keras (or similar Deep Learning frameworks).
- CNNs, RNNs, Transfer learning (for image classification, segmentation, object detection etc).
- Image Processing techniques using OpenCV or other white-box image feature extraction algorithms.
- End to end deployment of deep learning models.
Responsibilities:
- Improve robustness of Leena AI current NLP stack
- Increase zero shot learning capability of Leena AI current NLP stack
- Opportunity to add/build new NLP architectures based on requirements
- Manage End to End lifecycle of the data in the system till it achieves more than 90% accuracy
- Manage a NLP team
Page BreakRequirements:
- Strong understanding of linear algebra, optimisation, probability, statistics
- Experience in the data science methodology from exploratory data analysis, feature engineering, model selection, deployment of the model at scale and model evaluation
- Experience in deploying NLP architectures in production
- Understanding of latest NLP architectures like transformers is good to have
- Experience in adversarial attacks/robustness of DNN is good to have
- Experience with Python Web Framework (Django), Analytics and Machine Learning frameworks like Tensorflow/Keras/Pytorch.
- Experience with relational SQL & NoSQL databases including MySQL & MongoDB.
- Familiar with the basic principles of distributed computing and data modeling.
- Experience with distributed data pipeline frameworks like Celery, Apache Airflow, etc.
- Experience with NLP and NER models is a bonus.
- Experience building reusable code and libraries for future use.
- Experience building REST APIs.
Preference for candidates working in tech product companies