Senior Biostatistician (4+ years Exp.)

Company Detail:

Healthark Insights is a life sciences consulting firm established in 2016, by a team of consultants from

top-tier strategy firms with a cumulative experience of 100+ years. Overthe last few years, Healthark

has developed a team of expert analysts and consultants having deep domain expertise and technical

skills in helping pharma, medical devices, healthcare, and digital health organizations with some of their

key challenges in analytics,strategy, innovation, project management, and digital transformation areas.

We help our customers make critical decisions every day through expertise that combinesdeep domain

knowledge, rigorous research, and analysis, understanding of markets, technology, and experience.

With our experience and expertise, we not only provide insights but also work closely with our clientsto

execute the strategy that we have helpeddevelop.

We have worked with some of the top MNC organizations including Pfizer, Roche, Novartis,Eli Lilly,

Boehringer Ingelheim, Gilead, Walmart, IBM Watson Health, Abdul Lateef Jameel Group, Fresenius,

Deloitte, IQVIA, Decision Resources Group, and a many other mid-sized tosmall life sciences

organizations.

We have worked across projects in 60+ countriesincluding North America, Europe, MiddleAfrica, &

APAC region, bringing in a diverse set of experiences, perspectives, and at the same time understanding

of local nuances across each of these geographies.

Position: Biostatistician

Experience: 4+ years

Location: Ahmedabad / Bangalore

WorkingDays: Mon to Fri

Company Url: https://healtharkinsights.com/

Primary Responsibilities:

Collaborate with EG Directors, Research Analysts, RWE Analysts, and cross-functional

stakeholders to design RWE studies including retrospective/prospective observational studies,

pragmatic trials, comparative effectiveness research, or other types of real-world evidence studies.

Support or lead development of statistical methodologies in RWE studies, writing corresponding

sections in protocol/ SAP and executing analysis with relevant statistical tools.

Applying various statistical techniques and methodologies with appropriate statistical tools (R,

Python, SAS, etc.,) in RWD studies (survival analysis, propensity score matching, indirect

comparisons, causal inference statistics, multi-state modelling, etc.,)

Review and interpret statistical analysis results and generating meaningful insights from RWE

data. This includes summarizing findings and conclusions, identifying trends or patterns, and

drawing conclusions based on statistical evidence.

Provide biostatistics related input to and review of scientific deliverables (e.g., protocol, statistical

analysis plan, analyses, study report, publications) according to project timelines.

Contribute to development and provide oversight for statistical/programming code for complex

statistical analyses by utilizing statistical software packages such as R, SAS or Python.

Collaborating with cross-functional teams within Novartis including EG Directors, RWE Research

Analysts, data Analysts, clinicians, and other researchers, to ensure alignment on study

objectives, data requirements, and analytical approaches. Communicating findings and insights to

Novartis teams, regulatory agencies, or other relevant stakeholders.

Conducting quality control checks to ensure accuracy and reliability of statistical analyses. This

includes verifying data integrity, assessing assumptions of statistical models, and conducting

sensitivity analyses or robustness checks.

Minimum Requirements:

Master's degree in biostatistics, statistics, epidemiology, or a related field is required.

4+ years experience in similar role in biopharmaceutical companies, academia, healthcare provider

/ Payer / HTA, or relevant consultancy companies.

Proficiency in applying advanced statistical methods, including survival analysis, Cox modelling,

parametric survival modelling, regression modeling, longitudinal analysis, propensity score

matching, and Bayesian statistics.

Strong knowledge and experience in data analysis techniques, and statistical software packages

(such as R, SAS, or Python) are essential.

Familiarity with real-world data sources (such as electronic health records, claims databases,

registries, and observational studies) and experience working with real-world data to generate

evidence is crucial.

Understanding of study design principles, sample size calculations, randomization methods, and

statistical considerations for observational studies and clinical trials is an advantage.

Strong written and verbal communication skills to effectively collaborate with cross-functional

teams, present findings to non-technical stakeholders, and contribute to scientific publications

and conference presentations.

Knowledge of regulatory requirements, such as FDA guidelines and Good Clinical Practice (GCP)

standards, relevant to real-world evidence studies.