Data Scientist/Senior Data Scientist - Pharma

Job Responsibility:

- Apply a broad array of analytics skills including machine learning, statistics, text-mining/NLP, and modeling to extract insights from structured and unstructured data sources and complementary real-world & digital information streams to business challenges

- Developing and evolving core commercial models used across all analytics packages. Examples of the models will be: Multi-Channel Analytics, Patient Pathways, Omni-Channel Segmentation, Territory Design, Customer Targeting, Attribution Modeling, & Predictive Commercial Mix

- Design data test and learn experiments to drive personalized solutions across the customer journeys addressing key customer needs as well as enabling personalized experiences across each touch points through connective analytics.

- Collaborate across business to prototype, launch and Iterate analytics capabilities that quickly scale globally

- Lead the collaborate with local and global teams to ensure data driven decisions are embedded into business process.

- Automate analytics models and simplify information management

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.)

- Develop insights and storylines through translation of analysis and modeling

- End to end experience in retrieving data and developing an executive-level analysis through advanced analytics techniques

- Partner with leaders from across the business to translate the company's business objectives, market opportunities & portfolio of offerings into a formal, cohesive global commercial strategy

- Lead the development and implementation measurement planning aligning to strategy.

- Lead the collaborate with local and global teams to ensure data driven decisions are embedded into business process.

- We are looking for professionals with these skills to achieve our goals. If YOU have these skills, we would like to speak to you.

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

- Experience or understanding of end to end software tool/solution development and life cycle management

- Intellectual Curiosity

- Knowledge of the data landscape in healthcare (EHR, claims data, real world data, HEOR data)

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

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