HR Consultant at Recruitment Firm
Views:2691 Applications:54 Rec. Actions:Recruiter Actions:21
PMO Professional - Data Science - Consulting Firm (7-10 yrs)
Detailed role description :
- Work with stakeholders throughout the organization to identify opportunities for leveraging company data to drive business solutions.
- Mine and analyze data from company databases to drive optimization and improvement of product development, marketing techniques and business strategies.
- Assess the effectiveness and accuracy of new data sources and data gathering techniques.
- Develop custom data models and algorithms to apply to data sets.
- Use predictive modeling to increase and optimize customer experiences, revenue generation, ad targeting and other business outcomes.
- Work with extended development and visualization team to be able to train and develop data sciences capability in the team.
- Coordinate with different functional teams to implement models and monitor outcomes.
- Develop processes and tools to monitor and analyze model performance and data accuracy.
- Working knowledge in Machine Learning algorithms like Random Forest, Gradient Boosting, Neural Network etc.
- Have an understanding of econometric/statistical modeling and analysis techniques such as regression analysis, hypothesis testing, multivariate statistical analysis, time series techniques, optimization techniques, and statistical packages such as R, Python, SPARK
- Good comprehension skills to understand the practice leadership's business requirements. Industry expertise would be an added advantage.
- Define data requirements for creating a model and understand the business problem
- Clean, aggregate, analyze, interpret data and carry out quality analysis of it
- Analytical skills; detects, analyzes and solves work problems
TOP REQUIRED BEHAVIORS AND SKILLS
- 7-10 years of work experience in Data sciences/ predictive analytics with reputed organization is desirable.
- Good at managing a network of senior stakeholders and driving change through influencing skills
- Excellent written and verbal communication skills for coordinating across teams.
- Leadership and strategic thinking skills
- Strong problem solving skills with an emphasis on Sales and Offering development.
- Experience using statistical computer languages (R, Python, SLQ, etc.) to manipulate data and draw insights from large data sets.
- Experience working with and creating data architectures.
- Knowledge of a variety of machine learning techniques (clustering, decision tree learning, artificial neural networks, etc.) and their real-world advantages/drawbacks.
- Knowledge of advanced statistical techniques and concepts (regression, properties of distributions, statistical tests and proper usage, etc.) and experience with applications.
- A drive to learn and master new technologies and techniques.
- We're looking for someone with 7-10 years of experience manipulating data sets and building statistical models, has a Master's or PHD in Statistics, Mathematics, Computer Science or another quantitative field, and is familiar with the following software/tools:
- Coding knowledge and experience with several languages: C, C++, Java, JavaScript, etc.
- Knowledge and experience in statistical and data mining techniques: GLM/Regression, Random Forest, Boosting, Trees, text mining, social network analysis, etc.
- Experience querying databases and using statistical computer languages: R, Python, SQL, etc.
- Experience using web services: Redshift, S3, Spark, AWS, GCP etc.
- Experience creating and using advanced machine learning algorithms and statistics: regression, simulation, scenario analysis, modeling, clustering, decision trees, neural networks, etc.
- Experience analyzing data from 3rd party providers: Google Analytics, Site Catalyst, Coremetrics, Adwords, Crimson Hexagon, Facebook Insights, etc.
- Experience with distributed data/computing tools: Map/Reduce, Hadoop, Hive, Spark, Gurobi, MySQL, etc.
- Experience visualizing/presenting data for stakeholders using: Tableau, Qlik Sense, D3, Power BI, etc.
This job opening was posted long time back. It may not be active. Nor was it removed by the recruiter. Please use your discretion.