About Tradeindia.com - Infocom Network Ltd
We are looking for an experienced engineer to join our data science team, who will help us design, develop, and deploy machine learning models in production. You will develop robust models, prepare their deployment into production in a controlled manner, while providing appropriate means to monitor their performance and stability after deployment.
What You’ll Do will include (But not limited to):
- Preparing datasets needed to train and validate our machine learning models
- Anticipate and build solutions for problems that interrupt availability, performance, and stability in our systems, services, and products at scale.
- Defining and implementing metrics to evaluate the performance of the models, both for computing performance (such as CPU & memory usage) and for ML performance (such as precision, recall, and F1)
- Supporting the deployment of machine learning models on our infrastructure, including containerization, instrumentation, and versioning
- Supporting the whole lifecycle of our machine learning models, including gathering data for retraining, A/B testing, and redeployments
- Developing, testing, and evaluating tools for machine learning models deployment, monitoring, retraining.
- Working closely within a distributed team to analyze and apply innovative solutions over billions of documents
- Supporting solutions ranging from rule-bases, classical ML techniques to the latest deep learning systems.
- Partnering with cross-functional team members to bring large scale data engineering solutions to production
- Communicating your approach and results to a wider audience through presentations
- Demonstrated success with machine learning in a SaaS or Cloud environment, with hands–on knowledge of model creation and deployments in production at scale
- Good knowledge of traditional machine learning methods and neural networks
- Experience with practical machine learning modeling, especially on time-series forecasting, analysis, and causal inference.
- Experience with data mining algorithms and statistical modeling techniques for anomaly detection in time series such as clustering, classification, ARIMA, and decision trees is preferred.
- Ability to implement data import, cleansing and transformation functions at scale
- Fluency in Docker, Kubernetes
- Working knowledge of relational and dimensional data models with appropriate visualization techniques such as PCA.
- Solid English skills to effectively communicate with other team members
Due to the nature of the role, it would be nice if you have also:
- Experience with large datasets and distributed computing, especially with the Google Cloud Platform
- Fluency in at least one deep learning framework: PyTorch, TensorFlow / Keras
- Experience with No–SQL and Graph databases
- Experience working in a Colab, Jupyter, or Python notebook environment
- Some experience with monitoring, analysis, and alerting tools like New Relic, Prometheus, and the ELK stack
- Knowledge of Java, Scala or Go-Lang programming languages
- Familiarity with KubeFlow
- Experience with transformers, for example the Hugging Face libraries
- Experience with OpenCV
In a content critical age, Egnyte fuels business growth by enabling content-rich business processes, while also providing organizations with visibility and control over their content assets. Egnyte’s cloud-native content services platform leverages the industry’s leading content intelligence engine to deliver a simple, secure, and vendor-neutral foundation for managing enterprise content across business applications and storage repositories. More than 16,000 customers trust Egnyte to enhance employee productivity, automate data management, and reduce file-sharing cost and complexity. Investors include Google Ventures, Kleiner Perkins, Caufield & Byers, and Goldman Sachs. For more information, visit www.egnyte.com
Work closely with different Front Office and Support Function stakeholders including but not restricted to Business
Management, Accounts, Regulatory Reporting, Operations, Risk, Compliance, HR on all data collection and reporting use cases.
Collaborate with Business and Technology teams to understand enterprise data, create an innovative narrative to explain, engage and enlighten regular staff members as well as executive leadership with data-driven storytelling
Solve data consumption and visualization through data as a service distribution model
Articulate findings clearly and concisely for different target use cases, including through presentations, design solutions, visualizations
Perform Adhoc / automated report generation tasks using Power BI, Oracle BI, Informatica
Perform data access/transfer and ETL automation tasks using Python, SQL, OLAP / OLTP, RESTful APIs, and IT tools (CFT, MQ-Series, Control-M, etc.)
Provide support and maintain the availability of BI applications irrespective of the hosting location
Resolve issues escalated from Business and Functional areas on data quality, accuracy, and availability, provide incident-related communications promptly
Work with strict deadlines on high priority regulatory reports
Serve as a liaison between business and technology to ensure that data related business requirements for protecting sensitive data are clearly defined, communicated, and well understood, and considered as part of operational
prioritization and planning
To work for APAC Chief Data Office and coordinate with a fully decentralized team across different locations in APAC and global HQ (Paris).
Excellent knowledge of RDBMS and hands-on experience with complex SQL is a must, some experience in NoSQL and Big Data Technologies like Hive and Spark would be a plus
Experience with industrialized reporting on BI tools like PowerBI, Informatica
Knowledge of data related industry best practices in the highly regulated CIB industry, experience with regulatory report generation for financial institutions
Knowledge of industry-leading data access, data security, Master Data, and Reference Data Management, and establishing data lineage
5+ years experience on Data Visualization / Business Intelligence / ETL developer roles
Ability to multi-task and manage various projects simultaneously
Attention to detail
Ability to present to Senior Management, ExCo; excellent written and verbal communication skills
- Working closely with business stakeholders to define, strategize and execute crucial business problem statements which lie at the core of improvising current and future data-backed product offerings.
- Building and refining underwriting models for extending credit to sellers and API Partners in collaboration with the lending team
- Conceiving, planning and prioritizing data projects and manage timelines
- Building analytical systems and predictive models as a part of the agile ecosystem
- Testing performance of data-driven products participating in sprint-wise feature releases
- Managing a team of data scientists and data engineers to develop, train and test predictive models
- Managing collaboration with internal and external stakeholders
- Building data-centric culture from within, partnering with every team, learning deeply about business, working with highly experienced, sharp and insanely ambitious colleagues
What you need to have:
- B.Tech/ M.Tech/ MS/ PhD in Data Science / Computer Science, Statistics, Mathematics & Computation with a demonstrated skill-set in leading an Analytics and Data Science team from IIT, BITS Pilani, ISI
- 8+ years working in the Data Science and analytics domain with 3+ years of experience in leading a data science team to understand the projects to be prioritized, how the team strategy aligns with the organization mission;
- Deep understanding of credit risk landscape; should have built or maintained underwriting models for unsecured lending products
- Should have handled a leadership team in a tech startup preferably a fintech/ lending/ credit risk startup.
- We value entrepreneurship spirit: if you have had the experience of starting your own venture - that is an added advantage.
- Strategic thinker with agility and endurance
- Aware of the latest industry trends in Data Science and Analytics with respect to Fintech, Digital Transformations and Credit-lending domain
- Excellent command over communication is the key to manage multiple stakeholders like the leadership team, product teams, existing & new investors.
- Cloud Computing, Python, SQL, ML algorithms, Analytics and problem - solving mindset
- Knowledge and demonstrated skill-sets in AWS
Understand business problems and translate business requirements into technical requirements.
Conduct complex data analysis to ensure data quality & reliability i.e., make the data talk by extracting, preparing, and transforming it.
Identify, develop and implement statistical techniques and algorithms to address business challenges and add value to the organization.
Gather requirements and communicate findings in the form of a meaningful story with the stakeholders.
Build & implement data models using predictive modelling techniques. Interact with clients and provide support for queries and delivery
Lead and mentor data analysts.
What we are looking for-
Apart from your love for data and ability to code even while sleeping you would need the following.
Minimum of 02 years of experience in designing and delivery of data science solutions.
You should have successful projects of retail/BFSI/FMCG/Manufacturing/QSR in your kitty to show-off.
Deep understanding of various statistical techniques, mathematical models, and algorithms to start the conversation with the data in hand.
Ability to choose the right model for the data and translate that into a code using R, Python, VBA, SQL, etc.
Bachelors/Masters degree in Engineering/Technology or MBA from
Tier-1 B School or MSc. in Statistics or Mathematics.
- Building and operationalizing large scale enterprise data solutions and applications using one or more of AZURE data and analytics services in combination with custom solutions - Azure Synapse/Azure SQL DWH, Azure Data Lake, Azure Blob Storage, Spark, HDInsights, Databricks, CosmosDB, EventHub/IOTHub.
- Experience in migrating on-premise data warehouses to data platforms on AZURE cloud.
- Designing and implementing data engineering, ingestion, and transformation functions
Azure Synapse or Azure SQL data warehouse
Spark on Azure is available in HD insights and data bricks
- Experience with Azure Analysis Services
- Experience in Power BI
- Experience with third-party solutions like Attunity/Stream sets, Informatica
- Experience with PreSales activities (Responding to RFPs, Executing Quick POCs)
- Capacity Planning and Performance Tuning on Azure Stack and Spark.
- You hold an MS/Ph.D. degree in a STEM domain and 5+ years in a relevant position
- You share your ideas and continuously improve yourself and the team around you.
- Experienced in building and scaling data teams across multiple locations and domains.
- You have a good understanding of evolving an organization’s culture based on analytics and data insights
- Natural and comfortable leader, have excellent problem-solving, organizational, and analytical skills
- Passionate about data improving business and engineering practices like continuous delivery, traceability, and observability
- Strong communication skills, high integrity, and great attention to detail
You’ll get to work with:
- Consumer-facing, as well as core platform, finance, and distribution business units
- Marketing and product teams, across to our engineering teams
- Modern infrastructure (Kubernetes, AWS, GCP)
What we offer
- We offer you a chance to be part of a truly amazing journey in a company that sets very high targets and works hard to achieve them. You will be able to work with smart, motivated, and engaged co-workers from all over the world, in an intense and very energetic environment. This leads to you having a tangible impact on the way that we operate and expand our business.
Some of the highlights of the package include:
- Strong technical culture of continuous innovation and improvement
- Chance to become a shareholder of Gelato!
- Flexible festive holidays, swap days off according to your values and beliefs.
- Work at one of our hub city offices or even remotely
- And much more!
We are looking for an engineer with ML/DL background.
Ideal candidate should have the following skillset
3) Experience building and deploying systems
4) Experience with Theano/Torch/Caffe/Keras all useful
5) Experience Data warehousing/storage/management would be a plus
6) Experience writing production software would be a plus
7) Ideal candidate should have developed their own DL architechtures apart from using open source architechtures.
8) Ideal candidate would have extensive experience with computer vision applications
Candidates would be responsible for building Deep Learning models to solve specific problems. Workflow would look as follows:
1) Define Problem Statement (input -> output)
2) Preprocess Data
3) Build DL model
4) Test on different datasets using Transfer Learning
5) Parameter Tuning
6) Deployment to production
Candidate should have experience working on Deep Learning with an engineering degree from a top tier institute (preferably IIT/BITS or equivalent)
Octro Inc. is looking for a Data Scientist who will support the product, leadership and marketing teams with insights gained from analyzing multiple sources of data. The ideal candidate is adept at using 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 strong experience 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 have a proven ability to drive business results with their data-based insights.
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.
- Work with stakeholders throughout the organization to identify opportunities for leveraging company data to drive business solutions.
- Mine and analyze data from multiple 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 modelling to increase and optimize user experiences, revenue generation, ad targeting and other business outcomes.
- Develop various A/B testing frameworks and test model qualities.
- Coordinate with different functional teams to implement models and monitor outcomes.
- Develop processes and tools to monitor and analyze model performance and data accuracy.
- Strong problem solving skills with an emphasis on product development and improvement.
- Advanced knowledge of SQL and its use in data gathering/cleaning.
- Experience using statistical computer languages (R, Python, etc.) to manipulate data and draw insights from large data sets.
- Experience working with and creating data architectures.
- 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.
- Excellent written and verbal communication skills for coordinating across teams.
• Excellent understanding of machine learning techniques and algorithms, such as SVM, Decision Forests, k-NN, Naive Bayes etc.
• Experience in selecting features, building and optimizing classifiers using machine learning techniques.
• Prior experience with data visualization tools, such as D3.js, GGplot, etc..
• Good knowledge on statistics skills, such as distributions, statistical testing, regression, etc..
• Adequate presentation and communication skills to explain results and methodologies to non-technical stakeholders.
• Basic understanding of the banking industry is value add
Develop, process, cleanse and enhance data collection procedures from multiple data sources.
• Conduct & deliver experiments and proof of concepts to validate business ideas and potential value.
• Test, troubleshoot and enhance the developed models in a distributed environments to improve it's accuracy.
• Work closely with product teams to implement algorithms with Python and/or R.
• Design and implement scalable predictive models, classifiers leveraging machine learning, data regression.
• Facilitate integration with enterprise applications using APIs to enrich implementations