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Job Description

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

Your Qualifications:

• 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

About Egnyte

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

#LI-Remote

Responsibilities:

• Data science model review, run the code refactoring and optimization, containerization, deployment, versioning, and monitoring of its quality.
• Design and implement cloud solutions, build MLOps on the cloud (preferably AWS)
• Work with workflow orchestration tools like Kubeflow, Airflow, Argo, or similar tools
• Data science models testing, validation, and test automation.
• Communicate with a team of data scientists, data engineers, and architects, and document the processes.

Eligibility:

• Rich hands-on experience in writing object-oriented code using python
• Min 3 years of MLOps experience (Including model versioning, model and data lineage, monitoring, model hosting and deployment, scalability, orchestration, continuous learning, and Automated pipelines)
• Understanding of Data Structures, Data Systems, and software architecture
• Experience in using MLOps frameworks like Kubeflow, MLFlow, and Airflow Pipelines for building, deploying, and managing multi-step ML workflows based on Docker containers and Kubernetes.
• Exposure to deep learning approaches and modeling frameworks (PyTorch, Tensorflow, Keras, etc. )

About UpSolve

We built and deliver complex AI solutions which help drive business decisions faster and more accurately. We are a typical AI company and have a range of solutions developed on Video, Image and Text.

What you will do

• Stay informed on new technologies and implement cautiously
• Maintain necessary documentation for the project
• Fix the issues reported by application users
• Plan, build, and design solutions with a mental note of future requirements
• Coordinate with the development team to manage fixes, code changes, and merging

Location: Mumbai

Working Mode: Remote

What are we looking for

• Bachelor's or Master's degree in Computer Science, Software Engineering, or a related field.
• Minimum 2 years of professional experience in software development, with a focus on machine learning and full stack development.
• Strong proficiency in Python programming language and its machine learning libraries such as TensorFlow, PyTorch, or scikit-learn.
• Experience in developing and deploying machine learning models in production environments.
• Proficiency in web development technologies including HTML, CSS, JavaScript, and front-end frameworks such as React, Angular, or Vue.js.
• Experience in designing and developing RESTful APIs and backend services using frameworks like Flask or Django.
• Knowledge of databases and SQL for data storage and retrieval.
• Familiarity with version control systems such as Git.
• Strong problem-solving and analytical skills.
• Excellent communication and collaboration abilities.
• Ability to work effectively in a fast-paced and dynamic team environment.
• Good to have Cloud Exposure

Responsibilities

• Work on execution and scheduling of all tasks related to assigned projects' deliverable dates
• Optimize and debug existing codes to make them scalable and improve performance
• Design, development, and delivery of tested code and machine learning models into production environments
• Work effectively in teams, managing and leading teams
• Provide effective, constructive feedback to the delivery leader
• Manage client expectations and work with an agile mindset with machine learning and AI technology
• Design and prototype data-driven solutions

Eligibility

• Highly experienced in designing, building, and shipping scalable and production-quality machine learning algorithms in the field of Python applications
• Working knowledge and experience in NLP core components (NER, Entity Disambiguation, etc.)
• In-depth expertise in Data Munging and Storage (Experienced in SQL, NoSQL, MongoDB, Graph Databases)
• Expertise in writing scalable APIs for machine learning models
• Experience with maintaining code logs, task schedulers, and security
• Working knowledge of machine learning techniques, feed-forward, recurrent and convolutional neural networks, entropy models, supervised and unsupervised learning
• Experience with at least one of the following: Keras, Tensorflow, Caffe, or PyTorch

Title:- Data Scientist

Experience:-6 years

Work Mode:- Onsite

Primary Skills:- Data Science, SQL, Python, Data Modelling, Azure, AWS, Banking Domain (BFSI/NBFC)

Qualification:- Any

Roles & Responsibilities:-

1.  Acquiring, cleaning, and preprocessing raw data for analysis.

2.  Utilizing statistical methods and tools for analyzing and interpreting complex  datasets.

3.  Developing and implementing machine learning models for predictive analysis.

4.  Creating visualizations to effectively communicate insights to both technical and   non-technical stakeholders.

5.  Collaborating with cross-functional teams, including data engineers, business   analysts, and domain experts.

6.  Evaluating and optimizing the performance of machine learning models for   accuracy and efficiency.

7.  Identifying patterns and trends within data to inform business decision-making.

8.  Staying updated on the latest advancements in data science, machine learning, and  relevant technologies.

Requirement:- 

1.  Experience with modeling techniques such as Linear Regression, clustering, and classification techniques.

2.  Must have a passion for data, structured or unstructured.  0.6 – 5 years of hands-on experience with Python and SQL is a must.

3.   Should have sound experience in data mining, data analysis and machine learning techniques.

4.  Excellent critical thinking, verbal and written communications skills.

5.  Ability and desire to work in a proactive, highly engaging, high-pressure, client service environment.

6.   Good presentation skills.

Role: Principal Software Engineer

We looking for a passionate Principle Engineer - Analytics to build data products that extract valuable business insights for efficiency and customer experience. This role will require managing, processing and analyzing large amounts of raw information and in scalable databases. This will also involve developing unique data structures and writing algorithms for the entirely new set of products. The candidate will be required to have critical thinking and problem-solving skills. The candidates must be experienced with software development with advanced algorithms and must be able to handle large volume of data. Exposure with statistics and machine learning algorithms is a big plus. The candidate should have some exposure to cloud environment, continuous integration and agile scrum processes.

Responsibilities:

• Lead projects both as a principal investigator and project manager, responsible for meeting project requirements on schedule

• Software Development that creates data driven intelligence in the products which deals with Big Data backends

• Exploratory analysis of the data to be able to come up with efficient data structures and algorithms for given requirements

• The system may or may not involve machine learning models and pipelines but will require advanced algorithm development

• Managing, data in large scale data stores (such as NoSQL DBs, time series DBs, Geospatial DBs etc.)

• Creating metrics and evaluation of algorithm for better accuracy and recall

• Ensuring efficient access and usage of data through the means of indexing, clustering etc.

• Collaborate with engineering and product development teams.

Requirements:

• Master’s or Bachelor’s degree in Engineering in one of these domains - Computer Science, Information Technology, Information Systems, or related field from top-tier school

• OR Master’s degree or higher in Statistics, Mathematics, with hands on background in software development.

• Experience of 8 to 10 year with product development, having done algorithmic work

• 5+ years of experience working with large data sets or do large scale quantitative analysis

• Understanding of SaaS based products and services.

• Strong algorithmic problem-solving skills

• Able to mentor and manage team and take responsibilities of team deadline.

Skill set required:

• In depth Knowledge Python programming languages

• Understanding of software architecture and software design

• Must have fully managed a project with a team

• Having worked with Agile project management practices

• Experience with data processing analytics and visualization tools in Python (such as pandas, matplotlib, Scipy, etc.)

• Strong understanding of SQL and querying to NoSQL database (eg. Mongo, Casandra, Redis

• You're proficient in AI/Machine learning latest technologies  
• You're proficient in GPT-3 based algorithms 
• You have a passion for writing code as well as understanding and crafting the ways systems interact
• You believe in the benefits of agile processes and shipping code often
• You are pragmatic and work to coalesce requirements into reasonable solutions that provide value

• Deploy well-tested, maintainable and scalable software solutions
• Take end-to-end ownership of the technology stack and product 
• Collaborate with other engineers to architect scalable technical solutions
• Embrace and improve our standards and processes to reduce friction and unlock efficiency

About Us:

We are a VC-funded startup solving one of the biggest transportation problems India faces. Most passengers in India travel long distance by IRCTC trains. At time of booking, approx 1 out of every 2 passengers end up with a Waitlisted or RAC ticket. This creates a lot of anxiety for passengers, as Railway only announces only 4 hour before departure if they have a confirmed seat. We solve this problem through our Waitlist & RAC Protection. Protection can be bought against each IRCTC ticket at time of booking. If train ticket is not confirmed, we fly the passenger to the destination. Our team consists of 3 Founders from IIT, IIM and ISB.

Functional Experience:

• Computer Science or IT Engineering background with solid understanding of basics of Data Structures and Algorithms
• 2+ years of data science experience working with large datasets
• Expertise in Python packages like pandas, numPy, sklearn, matplotlib, seaborn, keras and tensorflow
• Expertise in Big Data technologies like Hadoop, Cassandra and PostgreSQL
• Expertise in Cloud computing on AWS with EC2, AutoML, Lambda and RDS
• Good knowledge of Machine Learning and Statistical time series analysis (optional)
• Unparalleled logical ability making you the go to guy for all things related to data
• You love coding like a hobby and are up for a challenge!

Cultural:

• Assume a strong sense of ownership of analytics : Design, develop & deploy
• Collaborate with senior management, operations & business team
• Ensure Quality & sustainability of the architecture
• Motivation to join an early stage startup should go beyond compensation

We’re looking to hire someone to help scale Machine Learning and NLP efforts at Episource. You’ll work with the team that develops the models powering Episource’s product focused on NLP driven medical coding. Some of the problems include improving our ICD code recommendations , clinical named entity recognition and information extraction from clinical notes.

This is a role for highly technical machine learning & data engineers who combine outstanding oral and written communication skills, and the ability to code up prototypes and productionalize using a large range of tools, algorithms, and languages. Most importantly they need to have the ability to autonomously plan and organize their work assignments based on high-level team goals.

You will be responsible for setting an agenda to develop and ship machine learning models that positively impact the business, working with partners across the company including operations and engineering. You will use research results to shape strategy for the company, and help build a foundation of tools and practices used by quantitative staff across the company.

What you will achieve:

• Define the research vision for data science, and oversee planning, staffing, and prioritization to make sure the team is advancing that roadmap

• Invest in your team’s skills, tools, and processes to improve their velocity, including working with engineering counterparts to shape the roadmap for machine learning needs

• Hire, retain, and develop talented and diverse staff through ownership of our data science hiring processes, brand, and functional leadership of data scientists

• Evangelise machine learning and AI internally and externally, including attending conferences and being a thought leader in the space

• Partner with the executive team and other business leaders to deliver cross-functional research work and models

Required Skills:

• Strong background in classical machine learning and machine learning deployments is a must and preferably with 4-8 years of experience

• Knowledge of deep learning & NLP

• Hands-on experience in TensorFlow/PyTorch, Scikit-Learn, Python, Apache Spark & Big Data platforms to manipulate large-scale structured and unstructured datasets.

• Experience with GPU computing is a plus.

• Professional experience as a data science leader, setting the vision for how to most effectively use data in your organization. This could be through technical leadership with ownership over a research agenda, or developing a team as a personnel manager in a new area at a larger company.

• Expert-level experience with a wide range of quantitative methods that can be applied to business problems.

• Evidence you’ve successfully been able to scope, deliver and sell your own research in a way that shifts the agenda of a large organization.

• Excellent written and verbal communication skills on quantitative topics for a variety of audiences: product managers, designers, engineers, and business leaders.

• Fluent in data fundamentals: SQL, data manipulation using a procedural language, statistics, experimentation, and modeling

Qualifications

• Professional experience as a data science leader, setting the vision for how to most effectively use data in your organization

• Expert-level experience with machine learning that can be applied to business problems

• Evidence you’ve successfully been able to scope, deliver and sell your own work in a way that shifts the agenda of a large organization

• Fluent in data fundamentals: SQL, data manipulation using a procedural language, statistics, experimentation, and modeling

• Degree in a field that has very applicable use of data science / statistics techniques (e.g. statistics, applied math, computer science, OR a science field with direct statistics application)

• 5+ years of industry experience in data science and machine learning, preferably at a software product company

• 3+ years of experience managing data science teams, incl. managing/grooming managers beneath you

• 3+ years of experience partnering with executive staff on data topics