• Charting learning journeys with knowledge graphs.
• Predicting memory decay based upon an advanced cognitive model.
• Ensure content quality via study behavior anomaly detection.
• Recommend tags using NLP for complex knowledge.
• Auto-associate concept maps from loosely structured data.
• Predict knowledge mastery.
• Search query personalization.
Requirements:
• 6+ years experience in AI/ML with end-to-end implementation.
• Excellent communication and interpersonal skills.
• Expertise in SageMaker, TensorFlow, MXNet, or equivalent.
• Expertise with databases (e. g. NoSQL, Graph).
• Expertise with backend engineering (e. g. AWS Lambda, Node.js ).
• Passionate about solving problems in education
About Tiger Analytics
Similar jobs
heads to solve complex business problems
- Develop statistical, and machine learning-based models/pipelines/methods to improve business
processes and engagements
- Conduct sophisticated data mining analyses of large volumes of data and build data science
models, as required, as part of the credit and risk underwriting solutions; customer engagement and
retention; new business initiatives; business process improvements
- Translate data mining results into a clear business-focused deliverable for decisionmakers
- Working with Application Developers on integrating machine learning algorithms and data mining
models into operational systems so it could lead to automation, productivity increase, and time
savings
- Provide the technical direction required to resolve complex issues to ensure the on-time delivery of
solutions that meet the business team’s expectations. May need to develop new methods to apply
to situations
- Knowledge of how to leverage statistical models in algorithms is a must
- Experience in multivariate analysis; identifying how several parameters can affect
retention/behaviour of the customer and identifying actions at different points of the customer lifecycle
Extensive experience coding in Python and having mentored teams to learn the same
- Great understanding of the data science landscape and what tools to leverage for different
problems
- A great structured thinker that could bring structure to any data science problem quickly
- Ability to visualize data stories and adept in data visualization tools and present insights as cohesive
stories to senior leadership
- Excellent capability to organize large data sets collected from many sources (web APIs and internal
databases) to get actionable insights
- Initiate data science programs in the team and collaborate across other data science teams to build
a knowledge database
Data Scientist – Delivery & New Frontiers Manager
Job Description:
We are seeking highly skilled and motivated data scientist to join our Data Science team. The successful candidate will play a pivotal role in our data-driven initiatives and be responsible for designing, developing, and deploying data science solutions that drives business values for stakeholders. This role involves mapping business problems to a formal data science solution, working with wide range of structured and unstructured data, architecture design, creating sophisticated models, setting up operations for the data science product with the support from MLOps team and facilitating business workshops. In a nutshell, this person will represent data science and provide expertise in the full project cycle. Expectation of the successful candidate will be above that of a typical data scientist. Beyond technical expertise, problem solving in complex set-up will be key to the success for this role.
Responsibilities:
- Collaborate with cross-functional teams, including software engineers, product managers, and business stakeholders, to understand business needs and identify data science opportunities.
- Map complex business problems to data science problem, design data science solution using GCP/Azure Databricks platform.
- Collect, clean, and preprocess large datasets from various internal and external sources.
- Streamlining data science process working with Data Engineering, and Technology teams.
- Managing multiple analytics projects within a Function to deliver end-to-end data science solutions, creation of insights and identify patterns.
- Develop and maintain data pipelines and infrastructure to support the data science projects
- Communicate findings and recommendations to stakeholders through data visualizations and presentations.
- Stay up to date with the latest data science trends and technologies, specifically for GCP companies
Education / Certifications:
Bachelor’s or Master’s in Computer Science, Engineering, Computational Statistics, Mathematics.
Job specific requirements:
- Brings 5+ years of deep data science experience
∙ Strong knowledge of machine learning and statistical modeling techniques in a in a clouds-based environment such as GCP, Azure, Amazon
- Experience with programming languages such as Python, R, Spark
- Experience with data visualization tools such as Tableau, Power BI, and D3.js
- Strong understanding of data structures, algorithms, and software design principles
- Experience with GCP platforms and services such as Big Query, Cloud ML Engine, and Cloud Storage
- Experience in configuring and setting up the version control on Code, Data, and Machine Learning Models using GitHub.
- Self-driven, be able to work with cross-functional teams in a fast-paced environment, adaptability to the changing business needs.
- Strong analytical and problem-solving skills
- Excellent verbal and written communication skills
- Working knowledge with application architecture, data security and compliance team.
- Bring in industry best practices around creating and maintaining robust data pipelines for complex data projects with/without AI component
- programmatically ingesting data from several static and real-time sources (incl. web scraping)
- rendering results through dynamic interfaces incl. web / mobile / dashboard with the ability to log usage and granular user feedbacks
- performance tuning and optimal implementation of complex Python scripts (using SPARK), SQL (using stored procedures, HIVE), and NoSQL queries in a production environment
- Industrialize ML / DL solutions and deploy and manage production services; proactively handle data issues arising on live apps
- Perform ETL on large and complex datasets for AI applications - work closely with data scientists on performance optimization of large-scale ML/DL model training
- Build data tools to facilitate fast data cleaning and statistical analysis
- Ensure data architecture is secure and compliant
- Resolve issues escalated from Business and Functional areas on data quality, accuracy, and availability
- Work closely with APAC CDO and coordinate with a fully decentralized team across different locations in APAC and global HQ (Paris).
You should be
- Expert in structured and unstructured data in traditional and Big data environments – Oracle / SQLserver, MongoDB, Hive / Pig, BigQuery, and Spark
- Have excellent knowledge of Python programming both in traditional and distributed models (PySpark)
- Expert in shell scripting and writing schedulers
- Hands-on experience with Cloud - deploying complex data solutions in hybrid cloud / on-premise environment both for data extraction/storage and computation
- Hands-on experience in deploying production apps using large volumes of data with state-of-the-art technologies like Dockers, Kubernetes, and Kafka
- Strong knowledge of data security best practices
- 5+ years experience in a data engineering role
- Science / Engineering graduate from a Tier-1 university in the country
- And most importantly, you must be a passionate coder who really cares about building apps that can help people do things better, smarter, and faster even when they sleep
- 3+ years of Experience majoring in applying AI/ML/ NLP / deep learning / data-driven statistical analysis & modelling solutions.
- Programming skills in Python, knowledge in Statistics.
- Hands-on experience developing supervised and unsupervised machine learning algorithms (regression, decision trees/random forest, neural networks, feature selection/reduction, clustering, parameter tuning, etc.). Familiarity with reinforcement learning is highly desirable.
- Experience in the financial domain and familiarity with financial models are highly desirable.
- Experience in image processing and computer vision.
- Experience working with building data pipelines.
- Good understanding of Data preparation, Model planning, Model training, Model validation, Model deployment and performance tuning.
- Should have hands on experience with some of these methods: Regression, Decision Trees,CART, Random Forest, Boosting, Evolutionary Programming, Neural Networks, Support Vector Machines, Ensemble Methods, Association Rules, Principal Component Analysis, Clustering, ArtificiAl Intelligence
- Should have experience in using larger data sets using Postgres Database.
XressBees – a logistics company started in 2015 – is amongst the fastest growing companies of its sector. Our
vision to evolve into a strong full-service logistics organization reflects itself in the various lines of business like B2C
logistics 3PL, B2B Xpress, Hyperlocal and Cross border Logistics.
Our strong domain expertise and constant focus on innovation has helped us rapidly evolve as the most trusted
logistics partner of India. XB has progressively carved our way towards best-in-class technology platforms, an
extensive logistics network reach, and a seamless last mile management system.
While on this aggressive growth path, we seek to become the one-stop-shop for end-to-end logistics solutions. Our
big focus areas for the very near future include strengthening our presence as service providers of choice and
leveraging the power of technology to drive supply chain efficiencies.
Job Overview
XpressBees would enrich and scale its end-to-end logistics solutions at a high pace. This is a great opportunity to join
the team working on forming and delivering the operational strategy behind Artificial Intelligence / Machine Learning
and Data Engineering, leading projects and teams of AI Engineers collaborating with Data Scientists. In your role, you
will build high performance AI/ML solutions using groundbreaking AI/ML and BigData technologies. You will need to
understand business requirements and convert them to a solvable data science problem statement. You will be
involved in end to end AI/ML projects, starting from smaller scale POCs all the way to full scale ML pipelines in
production.
Seasoned AI/ML Engineers would own the implementation and productionzation of cutting-edge AI driven algorithmic
components for search, recommendation and insights to improve the efficiencies of the logistics supply chain and
serve the customer better.
You will apply innovative ML tools and concepts to deliver value to our teams and customers and make an impact to
the organization while solving challenging problems in the areas of AI, ML , Data Analytics and Computer Science.
Opportunities for application:
- Route Optimization
- Address / Geo-Coding Engine
- Anomaly detection, Computer Vision (e.g. loading / unloading)
- Fraud Detection (fake delivery attempts)
- Promise Recommendation Engine etc.
- Customer & Tech support solutions, e.g. chat bots.
- Breach detection / prediction
An Artificial Intelligence Engineer would apply himself/herself in the areas of -
- Deep Learning, NLP, Reinforcement Learning
- Machine Learning - Logistic Regression, Decision Trees, Random Forests, XGBoost, etc..
- Driving Optimization via LPs, MILPs, Stochastic Programs, and MDPs
- Operations Research, Supply Chain Optimization, and Data Analytics/Visualization
- Computer Vision and OCR technologies
The AI Engineering team enables internal teams to add AI capabilities to their Apps and Workflows easily via APIs
without needing to build AI expertise in each team – Decision Support, NLP, Computer Vision, for Public Clouds and
Enterprise in NLU, Vision and Conversational AI.Candidate is adept at working with large data sets to find
opportunities for product and process optimization and using models to test the effectiveness of different courses of
action. They must have knowledge using a variety of data mining/data analysis methods, using a variety of data tools,
building, and implementing models, using/creating algorithms, and creating/running simulations. They must be
comfortable working with a wide range of stakeholders and functional teams. The right candidate will have a passion
for discovering solutions hidden in large data sets and working with stakeholders to improve business outcomes.
Roles & Responsibilities
● Develop scalable infrastructure, including microservices and backend, that automates training and
deployment of ML models.
● Building cloud services in Decision Support (Anomaly Detection, Time series forecasting, Fraud detection,
Risk prevention, Predictive analytics), computer vision, natural language processing (NLP) and speech that
work out of the box.
● Brainstorm and Design various POCs using ML/DL/NLP solutions for new or existing enterprise problems.
● Work with fellow data scientists/SW engineers to build out other parts of the infrastructure, effectively
communicating your needs and understanding theirs and address external and internal shareholder's
product challenges.
● Build core of Artificial Intelligence and AI Services such as Decision Support, Vision, Speech, Text, NLP, NLU,
and others.
● Leverage Cloud technology –AWS, GCP, Azure
● Experiment with ML models in Python using machine learning libraries (Pytorch, Tensorflow), Big Data,
Hadoop, HBase, Spark, etc
● Work with stakeholders throughout the organization to identify opportunities for leveraging company data to
drive business solutions.
● Mine and analyze data from company databases to drive optimization and improvement of product
development, marketing techniques and business strategies.
● Assess the effectiveness and accuracy of new data sources and data gathering techniques.
● Develop custom data models and algorithms to apply to data sets.
● Use predictive modeling to increase and optimize customer experiences, supply chain metric and other
business outcomes.
● Develop company A/B testing framework and test model quality.
● Coordinate with different functional teams to implement models and monitor outcomes.
● Develop processes and tools to monitor and analyze model performance and data accuracy.
● Develop scalable infrastructure, including microservices and backend, that automates training and
deployment of ML models.
● Brainstorm and Design various POCs using ML/DL/NLP solutions for new or existing enterprise problems.
● Work with fellow data scientists/SW engineers to build out other parts of the infrastructure, effectively
communicating your needs and understanding theirs and address external and internal shareholder's
product challenges.
● Deliver machine learning and data science projects with data science techniques and associated libraries
such as AI/ ML or equivalent NLP (Natural Language Processing) packages. Such techniques include a good
to phenomenal understanding of statistical models, probabilistic algorithms, classification, clustering, deep
learning or related approaches as it applies to financial applications.
● The role will encourage you to learn a wide array of capabilities, toolsets and architectural patterns for
successful delivery.
What is required of you?
You will get an opportunity to build and operate a suite of massive scale, integrated data/ML platforms in a broadly
distributed, multi-tenant cloud environment.
● B.S., M.S., or Ph.D. in Computer Science, Computer Engineering
● Coding knowledge and experience with several languages: C, C++, Java,JavaScript, etc.
● Experience with building high-performance, resilient, scalable, and well-engineered systems
● Experience in CI/CD and development best practices, instrumentation, logging systems
● Experience using statistical computer languages (R, Python, SLQ, etc.) to manipulate data and draw insights
from large data sets.
● Experience working with and creating data architectures.
● Good understanding of various machine learning and natural language processing technologies, such as
classification, information retrieval, clustering, knowledge graph, semi-supervised learning and ranking.
● Knowledge and experience in statistical and data mining techniques: GLM/Regression, Random Forest,
Boosting, Trees, text mining, social network analysis, etc.
● Knowledge on using web services: Redshift, S3, Spark, Digital Ocean, etc.
● Knowledge on creating and using advanced machine learning algorithms and statistics: regression,
simulation, scenario analysis, modeling, clustering, decision trees, neural networks, etc.
● Knowledge on analyzing data from 3rd party providers: Google Analytics, Site Catalyst, Core metrics,
AdWords, Crimson Hexagon, Facebook Insights, etc.
● Knowledge on distributed data/computing tools: Map/Reduce, Hadoop, Hive, Spark, MySQL, Kafka etc.
● Knowledge on visualizing/presenting data for stakeholders using: Quicksight, Periscope, Business Objects,
D3, ggplot, Tableau etc.
● Knowledge of a variety of machine learning techniques (clustering, decision tree learning, artificial neural
networks, etc.) and their real-world advantages/drawbacks.
● Knowledge of advanced statistical techniques and concepts (regression, properties of distributions,
statistical tests, and proper usage, etc.) and experience with applications.
● Experience building data pipelines that prep data for Machine learning and complete feedback loops.
● Knowledge of Machine Learning lifecycle and experience working with data scientists
● Experience with Relational databases and NoSQL databases
● Experience with workflow scheduling / orchestration such as Airflow or Oozie
● Working knowledge of current techniques and approaches in machine learning and statistical or
mathematical models
● Strong Data Engineering & ETL skills to build scalable data pipelines. Exposure to data streaming stack (e.g.
Kafka)
● Relevant experience in fine tuning and optimizing ML (especially Deep Learning) models to bring down
serving latency.
● Exposure to ML model productionzation stack (e.g. MLFlow, Docker)
● Excellent exploratory data analysis skills to slice & dice data at scale using SQL in Redshift/BigQuery.
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.
We’re building the future of private financial markets
Traditionally a space only for the wealthy and well-connected, we believe in a future where private markets are more accessible to investors and fundraisers. By leveling the playing field we hope to create a more equitable economy, where inspiring companies are connected to inspired investors, whoever and wherever they are.
Leveraging our trusted brand, global networks and incredible team, we’re building a technology-enabled ecosystem that is as diverse and dynamic as our investor network. As we progress on this ambitious journey, we’re looking for energetic and creative people to support and leave their mark on our platform.
Before Applying
- We have big plans to disrupt the traditional fundraising process for private businesses
- You will work with a diverse team of former investment bankers, strategy consultants and business owners in developing, monitoring, and improving products to facilitate the activity of private investing
- Everything we do is focused on helping build the private capital markets for the next generation of business owners and investors
- We work really hard but play really hard as well
Job purpose
- We are looking for passionate Data Scientists with strong problem-solving skills and prior experience in building machine learning models. You should possess the ability to thrive in a fast-paced environment. As a Data Scientist, working with passionate data-driven enthusiasts, you will lead the deployment of decision sciences with advanced analytics as well as machine learning and AI capabilities to support various lines of businesses. You will also help to enable a data driven culture within the organization.
Roles and responsibilities
- Work with other Data Scientists, Data Engineers, Data Analysts, Software engineers to build and manage data products
- Work on cross-functional projects using advanced data modeling and analysis techniques to discover insights that will guide strategic decisions and uncover optimization opportunities.
- Develop an enterprise data science strategy to achieve scale, synergies, and sustainability of model deployment
- Undertake rigorous analyses of business problems on structured and unstructured data with advanced quantitative techniques.
- Apply your expertise in data science, statistical analysis, data mining and the visualisation of data to derive insights that value-add to business decision making (e.g. hypothesis testing, development of MVPs, prototyping etc).
- Manage and optimize processes for data intake, validation, mining, and engineering as well as modeling, visualization and communication deliverable.
You’ll be a great fit for us if you
- Bachelor or Master’s in Computer Science, Statistics, Mathematics, Economics, or any other related fields
- At least 3 to 5 years of hands-on experience in a Data Science role with exposure and proficiency in quantitative and statistical analysis, predictive analytics,multi-variate testing and algorithm-optimization for machine learning
- Deep expertise in a range of ML concepts, frameworks and techniques such as logistic regression, clustering, dimensional reduction, recommendation systems,neural nets etc.
- Strong understanding of data infrastructure technologies (e.g. Spark, TensorFlow etc).
- Familiarity with data engineering methodologies, including SQL, ETL and experience in manipulating data sets with structured and unstructured data using Hadoop, AWS or other big data platforms.
- Highly proficient in data visualization and the use of dash boarding tools (e.g.Tableau, Matplotlib, plot.ly etc).
- Proven track record in delivering bespoke data science solutions in a cross-functional setting.
- Experience in managing a small team is preferred.
Bonus attributes
- Interested in dealing with data, including finding, and exploring more efficient ways/programs (e.g. machine learning) to collect, store, and analyse data
- Preferably have some understanding of terms in financial statements and financial ratios
- Strong problem-solving skills – able to find various ways to solve problems and decide which solution to move forward
- Ability to work under pressure and tight timings
- Team oriented, but highly independent for their own projects
- High level of organisational skills and ability to prioritize
2. Should understand the importance and know-how of taking the machine-learning-based solution to the consumer.
3. Hands-on experience with statistical, machine-learning tools and techniques
4. Good exposure to Deep learning libraries like Tensorflow, PyTorch.
5. Experience in implementing Deep Learning techniques, Computer Vision and NLP. The candidate should be able to develop the solution from scratch with Github codes exposed.
6. Should be able to read research papers and pick ideas to quickly reproduce research in the most comfortable Deep Learning library.
7. Should be strong in data structures and algorithms. Should be able to do code complexity analysis/optimization for smooth delivery to production.
8. Expert level coding experience in Python.
9. Technologies: Backend - Python (Programming Language)
10. Should have the ability to think long term solutions, modularity, and reusability of the components.
11. Should be able to work in a collaborative way. Should be open to learning from peers as well as constantly bring new ideas to the table.
12. Self-driven missile. Open to peer criticism, feedback and should be able to take it positively. Ready to be held accountable for the responsibilities undertaken.