Job location: Hartford, CT, Raleigh, NC or Remote, United States work from home.
Cigna’s Global Data & Analytics organization offers solutions that provide actionable insights to internal and external business partners and customers to reduce health costs, improve outcomes, and measure and forecast business performance.
In this role, you will work closely with customer advocacy and personalization strategy, product, operations and IT stakeholders to understand their strategy and provide recommendations on making data-driven decisions to meet their business objectives. The Senior Data Scientist will be responsible for conducting advanced analytics, research activities and clinical program evaluations in efforts to provide a positive customer engagement experience leading to positive health outcomes.
You will work both independently and collaboratively within the Customer Data Science team and business stakeholders, exercising professional judgment and initiative, and accountability for the delivery and reliability of their work.
So, you’re interested in becoming the newest member of this team? Great choice! Let’s check some boxes to see if you fit the bill:
- Perform advanced trend analytics to answer “ what happened?”
- Conduct root cause statistical analyses to answer “ why did this happen?”
- Develop predictive models to answer “ what will happen next?” and “ who is it likely to happen to?”
- Mine data to answer “ where are the opportunities?”
- Deploy innovation processes and pilot campaigns to answer “ how can we get better?”
You will be expected to have the requisite content knowledge related to evaluating program outcomes, statistical modeling techniques and health services research. You will interface with clinical, pricing, actuarial, reporting, product development, operations, and marketing departments as needed. The ideal candidate for this role will be highly motivated, flexible and embrace change.
Extraction and analysis of large healthcare claims data using tools such as SQL, R, Python, Teradata, Hadoop, SAS, etc.
Opportunity analysis including descriptive and multivariate statistics to identify patterns in the data
Design prospective and/or retrospective studies to determine the effectiveness of health program services and solutions i.e. matched-case control and randomized control trials
Creation of actionable insights and recommendations for business leaders
Presentation of results and recommendations to non-technical business partners and stakeholders to help drive the decision-making process and support business operations for internal and external customers.
Prior predictive modeling experience recommended. This position will work closely/alongside our Data Science Center of Excellence and work collaboratively on predictive opportunities.
- Advanced degree in Statistics, Math, Biostatistics, or Epidemiology highly preferred or equivalent and at least 5 years of experience in healthcare related analytics
- Experience with statistical software programming in SAS (SAS Enterprise Guide is a plus) is required—-Python or R is highly preferred
- Comprehension of healthcare data (e.g., ICD-9/ICD-10, claims, profiling), statistical analysis experience such as: multivariate and structural equation modeling and demonstrated understanding of health care and delivery system processes
- Working knowledge of program evaluation, predictive modeling, data mining, steerage, pharmacy and care management required – Match case Control study and Randomized Control trial
- To influence, drive, and improve the areas above, the person in this position will need to be self-motivated and possess strong leadership qualities
- Leadership competencies with the ability to collaborate well with others and establish working relationships, communicate effectively across the organization at different levels, think and act strategically, and influence key leaders
- Experience with linear modeling is preferred
Ability to successfully navigate and contribute in a highly-matrixed environment
- Emerging track record of presentations / publications preferred
- Strong customer focus and management of client expectations
Cigna Corporation (NYSE: CI) is a global health service company dedicated to improving the health, well-being and peace of mind of those we serve. We offer an integrated suite of health services through Cigna, Express Scripts, and our affiliates including medical, dental, behavioral health, pharmacy, vision, supplemental benefits, and other related products. Together, with our 74,000 employees worldwide, we aspire to transform health services, making them more affordable and accessible to millions. Through our unmatched expertise, bold action, fresh ideas and an unwavering commitment to patient-centered care, we are a force of health services innovation.
When you work with Cigna, you’ll enjoy meaningful career experiences that enrich people’s lives while working together to make the world a healthier place. What difference will you make? To see our culture in action, search #TeamCigna on Instagram.
Qualified applicants will be considered without regard to race, color, age, disability, sex, childbirth (including pregnancy) or related medical conditions including but not limited to lactation, sexual orientation, gender identity or expression, veteran or military status, religion, national origin, ancestry, marital or familial status, genetic information, status with regard to public assistance, citizenship status or any other characteristic protected by applicable equal employment opportunity laws.
If you require an accommodation based on your physical or mental disability please email: [email protected] Do not email [email protected] for an update on your application or to provide your resume as you will not receive a response.
Data Science Masters Program makes you proficient in tools and systems used by Data Science Professionals. It includes training on Statistics, Data Science, Python, Apache Spark & Scala, Tensorflow and Tableau. The curriculum has been determined by extensive research on 5000+ job descriptions across the globe.
What is data science, why is it so popular, and why did the Harvard Business Review hail it as the “sexiest job of the 21st century”? In this non-technical course, you’ll be introduced to everything you were ever too afraid to ask about this fast-growing and exciting field, without needing to write a single line of code. Through hands-on exercises, you’ll learn about the different data scientist roles, foundational topics like A/B testing, time series analysis, and machine learning, and how data scientists extract knowledge and insights from real-world data. So don’t be put off by the buzzwords. Start learning, gain skills in this hugely in-demand field, and discover why data science is for everyone!
What is data science and how can you use it to strengthen your organization? This course will teach you about the skills you need on your data team, and how you can structure that team to meet your organization’s needs. Data is everywhere! This course will provide you with an understanding of data sources your company can use and how to store that data. You’ll also discover ways to analyze and visualize your data through dashboards and A/B tests. To wrap up the course, we’ll discuss exciting topics in machine learning, including clustering, time series prediction, natural language processing (NLP), deep learning, and explainable AI! Along the way, you’ll learn about a variety of real-world applications of data science and gain a better understanding of these concepts through practical exercises.
The book begins by establishing the concept of cloud computing and describing the technological trends, and then discusses cloud computing architecture connotation and key technologies such as computing, storage, network, data, management, access, and security. With abundant project experiences and applications, the book is an essential reference for researchers and industrial engineers in computer science and information management.ed manner
The continuing importance of data analytics is not lost on higher education leaders, who face a multitude of challenges, including increasing operating costs, dwindling state support, limits to tuition increases, and increased competition from the for-profit sector. To navigate these challenges, savvy leaders must leverage data to make sound decisions. In Big Data on Campus, leading data analytics experts and higher ed leaders show the role that analytics can play in the better administration of colleges and universities.
The authors of Big Data with Hadoop MapReduce: A Classroom Approach have framed the book to facilitate understanding big data and MapReduce by visualizing the basic terminologies and concepts. They employed over 100 illustrations and many worked-out examples to convey the concepts and methods used in big data, the inner workings of MapReduce, and single node/multi-node installation on physical/virtual machines.
Is it possible to take advantage of the benefits of data collection–and mitigate risks–for both companies and customers?
Most consumers are not very skilled at evaluating privacy risks; they’re either unable to determine the cost of sharing personal data online or unaware of what they’re sharing. (Doesn’t everyone scroll down without reading to click “I accept”?) Without much intervention from most federal or state-level governments, companies are on their own to define what qualifies as reasonable use. In today’s digital surveillance economy, there are no clear-cut best practices or guidelines. Gathering and using information can help customers–we see that in personalization and autofill of online forms. But companies must act in the best interest of their customers and treat the sensitive information users give them with the ethical care of doctors, lawyers, and financial advisers. The challenges of operating in a digital ecosystem aren’t going away. Customer Data and Privacy: The Insights You Need from Harvard Business Review will help you understand the tangled interdependencies and complexities and develop strategies that allow your company to be good stewards, collecting, using, and storing customer data responsibly.