Sr Data Scientist

Job Purpose :

As a Global Commercial Data Scientist, you will lead and collaborate with others in discovery, development, scaling code based statistical modeling, machine learning or artificial intelligence capabilities to be leveraged by global business units and local markets. The primary focus of your efforts will be on streamlining strategic decision information, uncover new opportunities and automating commercial execution to ensure last mile value.

Qualifications :

- Bachelor's degree in computer science or related field, with 6+ years of demonstrated experience in developing product-level ML algorithms, with a proven track record of successfully delivering applied research to production (4+ with Masters)

- Strong written and verbal communication skills

- Excellent problem solving and data analysis skills, with expertise in developing or applying predictive analytics, statistical modeling, data and text mining/NLP, or machine learning algorithms on structured and unstructured data, at scale

- Applied research experience in machine learning or deep learning or information retrieval or data analytics

- Develop insights and storylines through translation of analysis and modeling

- Build and deploy tools and methodologies to create solutions enabling analytics and measurement of sales and marketing activities (e.g., market-mix modeling, cross-channel attribution, omni-channel segmentation and customer targeting, patient pathways, omni-channel customer journey analysis and optimization etc.)

- Hands on experience using Apache Spark, Python, R, SQL and Data Visualization tools

Relevant years of experience (pharma data sets)

Senior data scientist : 5 years in APLD

Data Scientist : 1 year in APLD

SKILLS & CAPABILITIES: PATIENT ANALYTICS

1) Expertise in APLD across all therapy areas (Examples below are illustrative & NOT exhaustive): MUST HAVE

NOTE : Expectation is that capability will not be restricted to using data & creating output. There should be a strong understanding of what the output means and to have judgement in assessing the reasonability and meaningfulness of output.

a) Patient eligibility

b) Look-back & look-forward rules

c) Source of Business

d) Alignment to zip, DMA, MSA, County, State, Provider

e) Patient adherence measures

f) Discontinuation analyses

g) Referral patterns

h) Refill gaps

i) Refill Patterns

j) Patient Pathway analysis

2) Technical Skills :

a) SQL : MUST HAVE

b) Python/R : MUST HAVE

3) Methodological capabilities

a) Correlation analyses : MUST HAVE

b) Segmentation & Clustering: MUST HAVE

c) Design of Test/Control framework: NICE to HAVE

d) Predictive modeling: MUST HAVE

e) Outlier Detection: MUST HAVE

f) Imputation & Simulation: MUST HAVE

4) Consultation on methodology/solution design for business questions: MUST HAVE

Data Assets beyond APLD-Xponent, DDD, SPP: MUST HAVE

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