Data Scientist - Digital Marketing & Customer Segmentation - BPO/KPO

Required Information Details :

Role : Data Scientist - Specialized In Digital Marketing

Required Technical Skill Set : Concepts of Statistics (Inferential and Descriptive)

- Time Series Analysis

- Cluster Analysis

- Tree based Algorithms

- Generalized Linear Models

- Neural Networks

- Natural Language Processing (NLP)(Very Important)

- Python\R (R is must)

- SQL

- Visualization (Using Python\R libraries, Tableau)

- Digital Marketing Data Science Techniques(specified below)

Desired Experience Range : 8+ years

Desired Competencies (Technical/Behavioral Competency) :

Must : 

- Have Concepts of Statistics, Cluster Analysis, Deep Learning, NLP, Recommender Systems, Hands on (Tensorflow, Keras, Pytorch), Optimization, Hyperparameter tuning techniques

- Specific to Digital Marketing (Data Driven Approach)- Email Marketing, Campaign Analysis\Recommendations, Customer Segmentation, Lead Analysis\Scoring, Personalized customer engagements, A\B Testing, Discounts\offers-real-time interactions, Sentiment Analysis, Budget Optimization, Customer Profiling and Interest Analysis, Recommendations for Search Engine Optimization

- Good-to-Have Presenting the storyline based on data to stakeholders, Agile Framework, DevOps, Knowledge of multidimensional models, Deep Learning Concepts, Big Data Concepts, Non textual Media Analytics

Role descriptions / Expectations from the Role :

1. Gathering and interpreting business requirements to design and create scalable data science solutions. Building mathematical models. Based on requirement deciding to use supervised or unsupervised learning techniques

2. Formulating data cleansing, transformations, feature creation and feature selection for a given business requirement.

3. Strong ability to work with unstructured data.

4. Excellent communication skills and ability to manage multiple, diverse stakeholders across business areas and leadership levels. Articulate the results, challenges, and concepts in a non-technical way

5. Guide the team of engineers, on implementation of the model in production environment

6. Ability to learn new concepts quickly in the field of data science\machine learning as it is evolving continuously

7. Working with product owners and suggesting them feature enhancements for lead to customer conversions

8. Using data driven techniques in Digital marketing for B2B interactions