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A Bachelor’s degree in data science, statistics, computer science, or a similar field
2+ years industry experience working in a data science role, such as statistics, machine learning,
deep learning, quantitative financial analysis, data engineering or natural language processing
Domain experience in Financial Services (banking, insurance, risk, funds) is preferred
Have and experience and be involved in producing and rapidly delivering minimum viable products,
results focused with ability to prioritize the most impactful deliverables
Strong Applied Statistics capabilities. Including excellent understanding of Machine Learning
techniques and algorithms
Hands on experience preferable in implementing scalable Machine Learning solutions using Python /
Scala / Java on Azure, AWS or Google cloud platform
Experience with storage frameworks like Hadoop, Spark, Kafka etc
Experience in building &deploying unsupervised, semi-supervised, and supervised models and be
knowledgeable in various ML algorithms such as regression models, Tree-based algorithms,
ensemble learning techniques, distance-based ML algorithms etc
Ability to track down complex data quality and data integration issues, evaluate different algorithmic
approaches, and analyse data to solve problems.
Experience in implementing parallel processing and in-memory frameworks such as H2O.ai
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
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Senior Data Scientist (Health Metrics)
at Biostrap
Introduction
The Biostrap platform extracts many metrics related to health, sleep, and activity. Many algorithms are designed through research and often based on scientific literature, and in some cases they are augmented with or entirely designed using machine learning techniques. Biostrap is seeking a Data Scientist to design, develop, and implement algorithms to improve existing metrics and measure new ones.
Job Description
As a Data Scientist at Biostrap, you will take on projects to improve or develop algorithms to measure health metrics, including:
- Research: search literature for starting points of the algorithm
- Design: decide on the general idea of the algorithm, in particular whether to use machine learning, mathematical techniques, or something else.
- Implement: program the algorithm in Python, and help deploy it.
The algorithms and their implementation will have to be accurate, efficient, and well-documented.
Requirements
- A Master’s degree in a computational field, with a strong mathematical background.
- Strong knowledge of, and experience with, different machine learning techniques, including their theoretical background.
- Strong experience with Python
- Experience with Keras/TensorFlow, and preferably also with RNNs
- Experience with AWS or similar services for data pipelining and machine learning.
- Ability and drive to work independently on an open problem.
- Fluency in English.
Machine Learning Architect - Deployments
at Netmeds.com
We are looking for an outstanding ML Architect (Deployments) with expertise in deploying Machine Learning solutions/models into production and scaling them to serve millions of customers. A candidate with an adaptable and productive working style which fits in a fast-moving environment.
Skills:
- 5+ years deploying Machine Learning pipelines in large enterprise production systems.
- Experience developing end to end ML solutions from business hypothesis to deployment / understanding the entirety of the ML development life cycle.
- Expert in modern software development practices; solid experience using source control management (CI/CD).
- Proficient in designing relevant architecture / microservices to fulfil application integration, model monitoring, training / re-training, model management, model deployment, model experimentation/development, alert mechanisms.
- Experience with public cloud platforms (Azure, AWS, GCP).
- Serverless services like lambda, azure functions, and/or cloud functions.
- Orchestration services like data factory, data pipeline, and/or data flow.
- Data science workbench/managed services like azure machine learning, sagemaker, and/or AI platform.
- Data warehouse services like snowflake, redshift, bigquery, azure sql dw, AWS Redshift.
- Distributed computing services like Pyspark, EMR, Databricks.
- Data storage services like cloud storage, S3, blob, S3 Glacier.
- Data visualization tools like Power BI, Tableau, Quicksight, and/or Qlik.
- Proven experience serving up predictive algorithms and analytics through batch and real-time APIs.
- Solid working experience with software engineers, data scientists, product owners, business analysts, project managers, and business stakeholders to design the holistic solution.
- Strong technical acumen around automated testing.
- Extensive background in statistical analysis and modeling (distributions, hypothesis testing, probability theory, etc.)
- Strong hands-on experience with statistical packages and ML libraries (e.g., Python scikit learn, Spark MLlib, etc.)
- Experience in effective data exploration and visualization (e.g., Excel, Power BI, Tableau, Qlik, etc.)
- Experience in developing and debugging in one or more of the languages Java, Python.
- Ability to work in cross functional teams.
- Apply Machine Learning techniques in production including, but not limited to, neuralnets, regression, decision trees, random forests, ensembles, SVM, Bayesian models, K-Means, etc.
Roles and Responsibilities:
Deploying ML models into production, and scaling them to serve millions of customers.
Technical solutioning skills with deep understanding of technical API integrations, AI / Data Science, BigData and public cloud architectures / deployments in a SaaS environment.
Strong stakeholder relationship management skills - able to influence and manage the expectations of senior executives.
Strong networking skills with the ability to build and maintain strong relationships with both business, operations and technology teams internally and externally.
Provide software design and programming support to projects.
Qualifications & Experience:
Engineering and post graduate candidates, preferably in Computer Science, from premier institutions with proven work experience as a Machine Learning Architect (Deployments) or a similar role for 5-7 years.
Sr. Manager Analytics
at Square Panda
Square Panda is a startup headquartered in Sunnyvale, CA with additional offices located in India
and China. We are in a research based Ed-Tech space, focusing on children's early literacy. We
have 3000+ schools under our belt and are proud to cater the needs of English Language
development of 70,000+ kids worldwide. Our multisensory neuroscience research-based phonics
learning system comes equipped with educational games to teach children many essential skills.
With a curriculum that has been specially adapted for Indian schools and children, we strive to
empower beginner learners through phonics awareness.
Responsibilities
o Lead the identification and execution of opportunities where Analytics can make a difference across the company with focus on multiple markets we operate in - US, India and China.
o Actively champion adoption of the analytics solutions across various markets and functions
o Translate business problems into Insights projects and lead in quantifying the various types of risk and rewards that allow these projects to be prioritized.
o Excellent project management and executive communication skills.
o Understanding of the techniques and technologies of data science, along with a detailed understanding of the challenges associated with each (e.g. overfitting, model refresh, challenge of acquiring training data, cost of compute, etc.)
o Proactively and continuously assess the marketplace and its dynamics, customers, and competitors.
o Provide an unbiased point of view on the performance of markets/brands, supported by facts and evidence
o Develop research strategies to ensure internal understanding of customer and competitor insights
o Develop and implement market research & analytical plans, in collaboration with cross‐functional teams.
o Seeks out alternative/creative ways of meeting an information need, considering new techniques to address business challenges.
o Monitor program risks and ensure appropriate actions are taken, including escalating issues timely to the management
o Maintain understanding of business operations and how users interact with the relevant systems and use that understanding to provide decision support analysis.
o Tap the working knowledge of AI and analytics to convey these business goals to the data professionals who will create the models and solutions.
o Enthusiasm, commitment, and business savvy to navigate the technical, political, and organizational roadblocks that can emerge.
Desired Profile
o 6-8 years of experience in executing multiple analytics projects end to end
o Entrepreneurial mind-set o Should have played hands on data sciences role in the past with full knowledge of which analytics technique to apply
o Background of core consulting / start up handling end to end projects o Excellent communication and project management skills o Ability to lead both business and analytics team to generate ROI and success for business o Expertise across the spectrum of analytics - Dashboards, Visualizations, Insights, Data Science driven AI, ML Projects
Senior Data Scientist
at Kaleidofin
• 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
Engineering Manager - Data
at Urbancompany (formerly known as Urbanclap)
Why are we building Urbancomapny?
Organized service commerce is a large yet young industry in India. While India is a very large market for a home and local services (~USD 50 Billion in retail spends) and expected to double in the next 5 years, there is no billion-dollar company in this segment today.
The industry is bare ~20 years old, with a sub-optimal market architecture typical of an unorganized market - fragmented supply side operated by middlemen. As a result, experiences are broken for both customers and service professionals, each largely relying upon word of mouth to discover the other. The industry can easily be 1.5-2x larger than it is today if the frictions in user and professional's journeys are removed - and the experiences made more meaningful and joyful.
The Urban Company team is young and passionate, and we see a massive disruption opportunity in his industry. By leveraging technology, and a set of simple yet powerful processes, we wish to build a platform that can organize the world of services - and bring them to your finger-tips. We believe there is the immense value (akin to serendipity) in bringing together customers and professionals looking for each other. In the process, we hope to impact the lives of millions of service entrepreneurs, and transform service commerce they way Amazon transformed product commerce.
Job Description :
Urbancompany has grown 3x YOY and so as our tech stack. We have evolved in data-driven approach solving for the product over the last few years. We deal with around 10TB in data analytics with around 50Mn/day. We adopted platform thinking pretty at the very early stage of UC. We started building central platform teams who are dedicated solve for core engineering problems around 2-3 years ago and now it has evolved to a full-fledged vertical. Out platform vertical majorly includes Data Engineering, Service and Core Platform, Infrastructure, and Security. We are looking for an Engineering Manager for the Data Engineering team currently. A person who loves solving standardization, have strong platform thinking, opinions, have solved for Data Engineering, Data Science and analytics platform.
Job Responsibilities
- Building high octane teams with high opinions and strong platform thinking
- Working on complex design and architectural problems.
- Solving funnel analytics, product insights and building a highly scalable data platform
- Experience in building Data Science Platform
- Highly productive data-driven models to contribute to product success and building
- Visioning out the roadmap and thought process behind taking current tech stack to next level
- Building and maintaining the high NPS of 70% of platform products
- Strong decision-maker with hands-on experience
- Think about abstractions, systems and services and write high-quality code.
- Have an understanding of loopholes in current systems/architecture that can potentially break in the future and push towards solving them with other stakeholders.
- Think through complex architecture to build robust platforms to serve together all the categories and flows, solve for scale, and work on internally build services to cater to our growing needs.
Job Requirements
- At least 1-2+ Years of experience in managing teams
- 5-8 years of experience in the industry solving complex problems from scratch and have graduate/post-graduate degrees from top-tier universities.
- A thinker with strong opinions and the ability to get those opinions into reality
- Prior experience of creating complex systems in the past.
- Ability to build scalable, sustainable, reliable, and secure products based on past experience and leading teams and projects by themselves.
- Ability to bring new practices, architectural choices, and new initiatives onto the table to make the overall tech stack more robust.
- History and familiarity with server-side architecture based on APIs, databases, infrastructure, and systems.
- Ability to own the technical road map for systems/components.
What can you expect?
- A phenomenal work environment, with massive ownership and growth opportunities.
- A high performance, high-velocity environment at the cutting edge of growth.
- Strong ownership expectation and freedom to fail.
- Quick iterations and deployments – fail-fast attitude.
- Opportunity to work on cutting edge technologies.
- The massive, and direct impact of the work you do on the lives of people.
