Fidelity - Director - Data Science Lead

BU Overview :

Data CoE stays abreast of emerging data technology and collaborates with multiple business units in handling and delivering projects to provide end-to-end data services in traditional and new data technologies also develop and deliver deep capability in big data technologies and data science.

Role Description:

The Data Science lead will be involved in complex projects, often across several business units and functions. This role demonstrates an expert level of data science knowledge as well as experience with data science techniques, systems and processes. The role will also require influencing, partner and collaborating with both Business and Technology. Data Scientists bridge the world of analytics and technology by focusing on building models and applications over raw or non-traditional data. The job responsibility includes:

- Leverage data to solve strategic, tactical, structured, and unstructured business problems

- Leverage statistical modelling and programming skills to derive business actionable insights out of large and/or structure, semi and unstructured datasets

- Identify/Help identify key business use cases in the environment which lend themselves to Machine learning.

- Solve complex problems by mining insights from all possible sources of information using statistical forecasting models and tools

- Collaborate with client and analytic teams to set analytic objectives, approaches, and work plans

- Research and evaluate new analytical methodologies, approaches, and solutions

- Collaborate with business and data owners to formulate problems whose solution would be impactful to the business

- Establish and coordinate full project life cycle plans for complex projects with multiple platforms.

- Influences team to commit to an environment of accountability and of delivering high quality work outputs that add value to the customer experience and organizational productivity

- Write and deliver reports on findings for technical and non-technical audiences.

Candidate Description :

- Bachelor / Master Degree in applied statistics, quantitative science, computer science, mathematics, management science, engineering, economics, or operations research (Master's and PhD preferred)

- Strong mathematical foundation in order to research and learn new modeling techniques

- 15 years of software development experience of which 7+ years in advanced analytics, model building, or marketing optimization

- Preferred experience working with big data analytics and big data platforms like Hadoop

- Experience with data manipulation and analysis tools such as Python, R, SQL, noSQL, Hadoop Ecosystem (MapReduce, Spark, Hive, Presto, Pig), Spark.

- Good working knowledge of several modelling techniques which may include: segmentation & clustering, mixed effect models, longitudinal data analysis, response & lift modelling, experimental design, Bayesian statistics, optimization techniques, text analytics, data mining

- Specialized Modelling:

a. Machine Learning: Advanced knowledge of machine learning algorithms, practical experience of parameter and performance tuning, Dimensionality Reduction, High Dimensionality Modelling, Ensembling ( Bagging, boosting, and stacking models to generate meta-models), Feature Engineering

b. Deep Learning : Deep machine learning models, using a deep graph with multiple processing layers & composed of multiple linear and non-linear models / techniques

c. AI Systems: Design and build production-ready AI systems with high level of accuracy and low level of maintenance

d. Big Data Models: Parallelization across multiple machines of approved models or techniques; non-trivial implementations such as those requiring estimation iterations

e. Time Series Analysis: Advanced time series modelling using nonlinear auto regression, Fourier transforms, change detection, or other approved techniques

f. Natural Language Processing: Implement state of the art techniques for processing natural language data for classification, entity extraction, or summarization

g. Outlier & Anomaly Detection: Implemented advanced techniques for outlier and anomaly detection including applications of clustering, learned rules.

- Strong critical thinking and problem solving skills

- Approaches problems with curiosity and open-mindedness

Education and Experience :

- Any prominent Bachelors or Masters degree

- 15 - 20 years of overall relevant IT experience.