Data Science

Data driven decision-making is core to advertising technology at AdElement. We are looking for sharp, disciplined, and highly quantitative machine learning/ artificial intellignce engineers with big data experience and a passion for digital marketing to help drive informed decision-making. You will work with top-talent and cutting edge technology and have a unique opportunity to turn your insights into products influencing billions. The potential candidate will have an extensive background in distributed training frameworks, will have experience to deploy related machine learning models end to end, and will have some experience in data-driven decision making of machine learning infrastructure enhancement. This is your chance to leave your legacy and be part of a highly successful and growing company.

Required Skills

- 3+ years of industry experience with Java/ Python in a programming intensive role

- 3+ years of experience with one or more of the following machine learning topics: classification, clustering, optimization, recommendation system, graph mining, deep learning

- 3+ years of industry experience with distributed computing frameworks such as Hadoop/Spark, Kubernetes ecosystem, etc

- 3+ years of industry experience with popular deep learning frameworks such as Spark MLlib, Keras, Tensorflow, PyTorch, etc

- 3+ years of industry experience with major cloud computing services

- An effective communicator with the ability to explain technical concepts to a non-technical audience

- (Preferred) Prior experience with ads product development (e.g., DSP/ad-exchange/SSP)

- Able to lead a small team of AI/ML Engineers to achieve business objectives

Responsibilities

- Collaborate across multiple teams - Data Science, Operations & Engineering on unique machine learning system challenges at scale

- Leverage distributed training systems to build scalable machine learning pipelines including ETL, model training and deployments in Real-Time Bidding space. 

- Design and implement solutions to optimize distributed training execution in terms of model hyperparameter optimization, model training/inference latency and system-level bottlenecks  

- Research state-of-the-art machine learning infrastructures to improve data healthiness, model quality and state management during the lifecycle of ML models refresh.

- Optimize integration between popular machine learning libraries and cloud ML and data processing frameworks. 

- Build Deep Learning models and algorithms with optimal parallelism and performance on CPUs/ GPUs.

- Work with top management on defining teams goals and objectives.

Education

- MTech or Ph.D. in Computer Science, Software Engineering, Mathematics or related fields

Title: Data Engineer (Azure) (Location: Gurgaon/Hyderabad)

Salary: Competitive as per Industry Standard

We are expanding our Data Engineering Team and hiring passionate professionals with extensive

knowledge and experience in building and managing large enterprise data and analytics platforms. We

are looking for creative individuals with strong programming skills, who can understand complex

business and architectural problems and develop solutions. The individual will work closely with the rest

of our data engineering and data science team in implementing and managing Scalable Smart Data

Lakes, Data Ingestion Platforms, Machine Learning and NLP based Analytics Platforms, Hyper-Scale

Processing Clusters, Data Mining and Search Engines.

What You’ll Need:

• 3+ years of industry experience in creating and managing end-to-end Data Solutions, Optimal

Data Processing Pipelines and Architecture dealing with large volume, big data sets of varied

data types.

• Proficiency in Python, Linux and shell scripting.
• Strong knowledge of working with PySpark dataframes, Pandas dataframes for writing efficient pre-processing and other data manipulation tasks.
  ● Strong experience in developing the infrastructure required for data ingestion, optimal

extraction, transformation, and loading of data from a wide variety of data sources using tools like Azure Data Factory,  Azure Databricks (or Jupyter notebooks/ Google Colab) (or other similiar tools).

• Working knowledge of github or other version control tools.
• Experience with creating Restful web services and API platforms.
• Work with data science and infrastructure team members to implement practical machine

learning solutions and pipelines in production.

• Experience with cloud providers like Azure/AWS/GCP.
• Experience with SQL and NoSQL databases. MySQL/ Azure Cosmosdb / Hbase/MongoDB/ Elasticsearch etc.
• Experience with stream-processing systems: Spark-Streaming, Kafka etc and working experience with event driven architectures.
• Strong analytic skills related to working with unstructured datasets.

Good to have (to filter or prioritize candidates)

• Experience with testing libraries such as pytest for writing unit-tests for the developed code.
• Knowledge of Machine Learning algorithms and libraries would be good to have,

implementation experience would be an added advantage.

• Knowledge and experience of Datalake, Dockers and Kubernetes would be good to have.
• Knowledge of Azure functions , Elastic search etc will be good to have.

• Having experience with model versioning (mlflow) and data versioning will be beneficial
• Having experience with microservices libraries or with python libraries such as flask for hosting ml services and models would be great.

About Turing:

Turing enables U.S. companies to hire the world’s best remote software engineers. 100+ companies including those backed by Sequoia, Andreessen, Google Ventures, Benchmark, Founders Fund, Kleiner, Lightspeed, and Bessemer have hired Turing engineers. For more than 180,000 engineers across 140 countries, we are the preferred platform for finding remote U.S. software engineering roles. We offer a wide range of full-time remote opportunities for full-stack, backend, frontend, DevOps, mobile, and AI/ML engineers.

We are growing fast (our revenue 15x’d in the past 12 months and is accelerating), and we have raised $14M in seed funding (https://tcrn.ch/3lNKbM9">one of the largest in Silicon Valley) from:

• Facebook’s 1st CTO and Quora’s Co-Founder (Adam D’Angelo)
• Executives from Google, Facebook, Square, Amazon, and Twitter
• Foundation Capital (investors in Uber, Netflix, Chegg, Lending Club, etc.)
• Cyan Banister
• Founder of Upwork (Beerud Sheth)

We also raised a much larger round of funding in October 2020 that we will be publicly announcing over the coming month. 

Some articles about Turing:

• https://techcrunch.com/2020/08/25/turing-raises-14m-to-help-source-vet-place-and-manage-remote-developers-in-tech-jobs/">TechCrunch: Turing raises $14M seed to help source, vet, place, and manage remote developers
• https://www.theinformation.com/articles/six-startups-prospering-during-coronavirus">The Information: Six Startups Prospering During Coronavirus
• https://medium.com/@cyanbanister/turing-helps-the-world-level-up-ff44b4e6415d">Cyan Banister: Turing Helps the World Level Up
• https://turing.com/boundarylessblog/2019/10/the-future-of-work-is-remote/the-future-of-work/">Jonathan Siddharth (Turing CEO): The Future of Work is Remote.

Turing is led by successful repeat founders Jonathan Siddharth and Vijay Krishnan, whose last A.I. company leveraged elite remote talent and had a successful acquisition. (https://techcrunch.com/2017/02/23/revcontent-acquires-rover/">Techcrunch story). Turing’s leadership team is composed of ex-Engineering and Sales leadership from Facebook, Google, Uber, and Capgemini.

About the role:

Software developers from all over the world have taken 200,000+ tests and interviews on Turing. Turing has also recommended thousands of developers to its customers and got customer feedback in terms of customer interview pass/fail data and data from the success of the collaboration with a U.S customer. This generates a massive proprietary dataset with a rich feature set comprising resume and test/interview features and labels in the form of actual customer feedback. Continuing rapid growth in our business creates an ever-increasing data advantage for us. 

We are looking for a Machine Learning Scientist who can help solve a whole range of exciting and valuable machine learning problems at Turing. Turing collects a lot of valuable heterogeneous signals about software developers including their resume, GitHub profile and associated code and a lot of fine-grained signals from Turing’s own screening tests and interviews (that span various areas including Computer Science fundamentals, project ownership and collaboration, communication skills, proactivity and tech stack skills), their history of successful collaboration with different companies on Turing, etc. 

A machine learning scientist at Turing will help create deep developer profiles that are a good representation of a developer’s strengths and weaknesses as it relates to their probability of getting successfully matched to one of Turing’s partner companies and having a fruitful long-term collaboration. The ML scientist will build models that are able to rank developers for different jobs based on their probability of success at the job. 

     
 You will also help make Turing’s tests more efficient by assessing their ability to predict the probability of a successful match of a developer with at least one company. The prior probability of a registered developer getting matched with a customer is about 1%. We want our tests to adaptively reduce perplexity as steeply as possible and move this probability estimate rapidly toward either 0% or 100%; maximize expected information-gain per unit time in other words. 

As an ML Scientist on the team, you will have a unique opportunity to make an impact by advancing ML models and systems, as well as uncovering new opportunities to apply machine learning concepts to Turing product(s).

This role will directly report to Turing’s founder and CTO, https://www.linkedin.com/in/vijay0/">Vijay Krishnan. This is his https://scholar.google.com/citations?user=uCRc7DgAAAAJ&;hl=en">Google Scholar profile. 

Responsibilities:

• Enhance our existing machine learning systems using your core coding skills and ML knowledge.
• Take end to end ownership of machine learning systems - from data pipelines, feature engineering, candidate extraction, model training, as well as integration into our production systems.
• Utilize state-of-the-art ML modeling techniques to predict user interactions and the direct impact on the company’s top-line metrics.
• Design features and builds large scale recommendation systems to improve targeting and engagement.
• Identify new opportunities to apply machine learning to different parts of our product(s) to drive value for our customers.

Minimum Requirements:

• BS, MS, or Ph.D. in Computer Science or a relevant technical field (AI/ML preferred).
• Extensive experience building scalable machine learning systems and data-driven products working with cross-functional teams
• Expertise in machine learning fundamentals, applicable to search - Learning to Rank, Deep Learning, Tree-Based Models, Recommendation Systems, Relevance and Data mining, understanding of NLP approaches like W2V or Bert.
• 2+ years of experience applying machine learning methods in settings like recommender systems, search, user modeling, graph representation learning, natural language processing.
• Strong understanding of neural network/deep learning, feature engineering, feature selection, optimization algorithms. Proven ability to dig deep into practical problems and choose the right ML method to solve them.
• Strong programming skills in Python and fluency in data manipulation (SQL, Spark, Pandas) and machine learning (scikit-learn, XGBoost, Keras/Tensorflow) tools.
• Good understanding of mathematical foundations of machine learning algorithms.
• Ability to be available for meetings and communication during Turing's "coordination hours" (Mon - Fri: 8 am to 12 pm PST).

Other Nice-to-have Requirements:

• First author publications in ICML, ICLR, NeurIPS, KDD, SIGIR, and related conferences/journals. 
• Strong performance in Kaggle competitions.
• 5+ years of industry experience or a Ph.D. with 3+ years of industry experience in applied machine learning in similar problems e.g. ranking, recommendation, ads, etc.
• Strong communication skills.
• Experienced in leading large-scale multi-engineering projects.
• Flexible, and a positive team player with outstanding interpersonal skills.

• Hands-on experience in Development
• 4-6 years of Hands on experience with Python scripts
• 2-3 years of Hands on experience in PySpark coding. Worked in spark cluster computing technology.
• 3-4 years of Hands on end to end data pipeline experience working on AWS environments
• 3-4 years of Hands on experience working on AWS services – Glue, Lambda, Step Functions, EC2, RDS, SES, SNS, DMS, CloudWatch etc.
• 2-3 years of Hands on experience working on AWS redshift
• 6+ years of Hands on experience with writing Unix Shell scripts
• Good communication skills

• Create, maintain and automate datasets and insightful dashboards to track core metrics and extract business insights
• Analyze large-scale structured and unstructured data to identify business opportunities and optimize features for Analytics.

• 8+ years experience doing Business Intelligence and Analytics work
• B.Tech/M.Tech in a technical field (Computer Science, Math, Statistics)
• Strong knowledge in Data Design, Data Modelling, and Data Validation best practices
• Proficient in data visualization, Preferably SSRS & Power BI
• Fluency with writing advanced SQL code
• Experience with SQL Server Administration and best practices
• Possess BFSI, Fintech Domain Knowledge
• Excellent interpersonal, cross-functional, communication, writing, and presentation skills
• Comfortable working in a fast-paced environment with the ability to be a team player
• Possess excellent Project Management and superior Team Management skills