Position: Data Engineer (1464)
Company: Exadel
Category: Data Science
Experience required: 5y
English level: upper

**About Our Customer:**
The customer is a leader in actionable analytics for sales effectiveness. It is revolutionizing how field sales teams make their day-to-day decisions, providing them with real-time, in-context, and in-workflow smart suggestions that have been shown to meaningfully improve rep performance. Smart suggestions, driven by data, help reps better navigate sales decisions such as which prospects to call, when and where to call on them, what products to position, which messages to use, and why.

**About Our Project:**
Our ideal candidate will have extensive knowledge of Big Data and ML; specifically, how to organize and architect ML modules. Our ML-specialized team only works on ML projects and has active and case-studies proven ML modules in production (and is working on new ones all the time). We're looking for a talented professional to bring strong architectural advice to the table.

ML modules in the Customer’s company are undergoing real case-studies after deployment to prove the concept. And the results are driving real-world engagement increase. For reference - "just" 4% engagement increase is a huge win in a multi-billion and highly competitive industry.

The Customer started as a provider of smart suggestions to pharma sales about the ways they do engagements. Initially, 100% of engagements were done on statically coded business rules, defined per customer via iterative engagement with the latter. Now, more and more modules are getting ML-driven enhancements on top of those static rules. So, at the moment, engagements are done through a combination of methodology. For example, the engine decides that it is time to send an email to a doctor, but ML model decides which template is more likely to appeal to a given doctor and which days they most often open promotional materials as opposed to which days they never read them. The same can be done in regards to real visits, medical conference invites, etc.

**Project Team:**
This is a dedicated scrum team with about 5 engineers (several ML-related PhDs among them), 2 QAs, and a scrum master.

**Requirements:**
- 3-5 years of relevant experience with a proven track record of developing algorithms and machine learning products for production-ready recommendation or prediction systems using languages and big data platforms such as Scala, **Python**, R, Java, **Spark**, Cassandra, and Hadoop.
- Deep understanding and experience in big data concepts
- Extensive knowledge of ML frameworks, libraries (e.g. Scikit, Numpy, Pandas, TensorFlow, R ML packages), tools (**AWS EMR**, Databricks, Knime, etc), data structures, and data modeling concepts
- Able to organize and architect ML modules 
- Knowledge of mathematics, statistics, and algorithms (Random Forests, General Linear Regression, Elastic Nets, Clustering, and NLP)
- Design/use schemas in various data storage platforms (MySQL, DataLake, S3, Snowflake)
- Experience in design/development of RESTful APIs in Java(Spring)

**Nice to have:**
- Superb analytical and problem-solving abilities
- Great communication, collaboration skills, time management, and organizational abilities
- BS/MS in computer science, ML, mathematics, or related field and/or equivalent work experience
- Lead role in one or more projects, architecting/designing middle-tier, ML, or database layers for a new application.

Software engineers with experience working with machine learning applications
Math background and understanding of algorithms is a plus


**Responsibilities:**
- Analyze and improve existing data science modules
- Design new machine learning modules according to requirements
- Research and advise on appropriate ML algorithms, tools, and integration/deployment strategies
- Select appropriate datasets and data representation methods
- Run machine learning tests and experiments
- Use test results to perform statistical analysis and fine-tuning
- Train and retrain systems when necessary
- Extend existing ML libraries and frameworks
- Keep abreast of developments in the field
50% - Take new moled that Data Science team has created and productize it
25% - enhance the model, working with data science team
25% - engineering - supporting qa, docs ect

**Company offers:**
- Vacation is 20 working days / till 20 working days per year for sick leaves
- Full payment of taxes
- English courses
- Flexible work schedule
- Friendly environment
- Medical insurance
- Opportunity for career growth