01
Clinical research & ICH-GCP
ICH E6(R2)Drug development phases, the roles of sponsor, CRO, investigator and IRB/EC, protocol structure, informed consent, and the thirteen GCP principles that sit behind every data decision.
Now enrolling — weekday, weekend and live online batches
Don't just learn Clinical Data Management. Experience it.
Our students don't only study CDM concepts. They work inside a structured, work-style environment — daily task assignments, real study scenarios, deliverables, review cycles and deadlines — so they understand how clinical data work actually happens inside an organisation before they ever walk into one.
Real-world workflows
Daily practical tasks
Project-based training
Industry-style environment
Weekday, weekend and live online batches · Call or WhatsApp +91 98666 87373
Start with a free counselling call
Tell us your background and we will call you back with current batch dates, fees and an honest read on whether this fits you.
Prefer to talk? Call or WhatsApp +91 98666 87373 — we reply the same day.
Eligibility
Who can join
Clinical data management is one of the few clinical research functions that genuinely recruits freshers. If your degree is on this list you are eligible — we start from the protocol and assume no prior exposure to trials, EDC systems or coding.
01
Pharmacy
- B.Pharm
- M.Pharm
- Pharm.D
02
Life sciences
- B.Sc and M.Sc — life sciences
- Biotechnology
- Microbiology
- Biochemistry
- Zoology and botany
03
Nursing & allied health
- Nursing — B.Sc and M.Sc
- BPT — physiotherapy
04
Medical & dental
- MBBS
- BDS
- BAMS
- BHMS
No coding background needed. Nothing on the programme assumes you have written a line of code.
Final-year students welcome. You can join the weekend batch before your results are out.
Working professionals too. Weekend and live online batches exist for exactly this.
- The model
- Learn → Practise → ExperienceConcepts, then the work itself
- Duration
- 3 monthsWeekday, weekend or live online
- Daily tasks
- AssignedWith an objective, a deliverable and a deadline
- Every deliverable
- ReviewedSubmit → review → feedback → correct
- The project
- One full studySetup through to database lock
The difference
Experience the work before you get the job
The hardest part of a first CDM role is rarely the terminology. It is not knowing how the work actually happens — what lands on your desk in the morning, what "done" looks like, who reviews it, and what happens when you get it wrong.
How training usually runs
Classroom- Learn the theory
- Watch a demonstration
- Complete a set exercise
- Finish the course
- Start applying for jobs
You can understand every concept and still freeze in week one, because nothing in the course looked like a working day.
How Clini Careers runs
Work environment- Learn the concept
- Enter a structured project environment
- Receive the day's assigned tasks
- Perform the workflow and submit the deliverable
- Get reviewed, take the feedback, correct it
- Work to a timeline, alongside a team
By the time you finish, the shape of the job is familiar — because you have already worked it.
01 — Experience
Experience
See how data management work flows through a study, end to end, rather than as twelve unrelated topics.
02 — Practice
Practice
Perform the tasks yourself — review, queries, validation, coding, reconciliation, documentation.
03 — Prepare
Prepare
Build the workplace habits and the scenario answers that interviews are actually built around.
04 — Grow
Grow
Leave with a documented project, a working vocabulary and a realistic view of the career ahead.
See how it works
One task, from assignment to sign-off
Every practical activity on the programme moves through the same seven steps — the same loop a data management team runs on a live study. Click through it.
Step 01 · Assignment
The task arrives with a scope and a deadline
You are not told "practise query management". You are given a defined activity on the study you are working — which subjects, which forms, which checks, and by when. Exactly as work is handed over on a project.
Step 02 · Requirement
Work out what "done" means before you start
You read the instruction the way you would read an SOP or a Data Validation Plan section: what is in scope, what the acceptance criteria are, what documentation has to exist at the end. Asking the right question here is a skill in itself.
Step 03 · Execution
Do the actual work
Review the data. Find the discrepancy. Write the query text so a site can act on it. Run the check. Code the term. Reconcile the listing. This is the part most courses replace with a demonstration.
Step 04 · Deliverable
Submit something a reviewer can open
A tracker, an annotated form, a query log, a validation record, a written summary. Work that exists only in your head does not count on a project, and it does not count here.
Step 05 · Review
Someone checks your work — properly
Your trainer reviews the deliverable the way a senior would: is it complete, is it accurate, is it traceable, would it survive an audit. You get written comments, not a mark out of ten.
Step 06 · Correction
Respond to the feedback and rework it
You address each comment, correct the work and resubmit. Learning to take review comments without taking them personally is one of the most useful professional habits we can hand you.
Step 07 · Sign-off
Approved, and it stays in your project file
The task closes and the artefact joins the project you will talk about in interviews. Twelve modules of this, and you have a body of work rather than a certificate.
No ambiguity
What will you actually do at Clini Careers?
Not what you will "be exposed to" or "gain an understanding of". This is the list of things you will personally have done by the end of the programme.
- Receive assigned tasks with deadlines
- Work realistic clinical data scenarios
- Follow defined workflows and SOP-style instructions
- Review subject data across forms and visits
- Identify discrepancies and decide what they mean
- Write, track and close queries
- Design and test edit checks, then run UAT
- Code adverse events and medications
- Reconcile external and vendor data
- Complete the documentation that has to exist
- Submit deliverables for review
- Receive written feedback and correct your work
- Work to a timeline you did not set
- Report status the way a team expects to hear it
- Take one study from setup to database lock
A representative training day
What if you could experience a CDM workday before joining a company?
Working days differ between organisations, studies and phases — there is no single correct schedule. What follows is a representative training day on our programme, built to mirror the rhythm of project work: tasks in the morning, execution through the day, review before you close.
Open the study, read what changed overnight, and look at what is sitting in your queue. Before you touch anything you should know where the study is: which visits are entering data, which queries are ageing, what is blocking someone else.
Outcome: you know your day before it starts
You are handed the day's practical activities, each with a scope, an expected deliverable and a time by which it is due. Some days that is a clean run of data review. Some days it is a validation activity that will take you three hours and one that will take you twenty minutes.
Outcome: a defined workload with owners and deadlines
Go through subject data listing by listing: missing fields, impossible dates, values that contradict another form, an adverse event that ends before it starts. Reviewing data well is a trained skill — it is pattern recognition, and it only comes from volume.
Outcome: a list of discrepancies worth raising
Decide which discrepancies are genuine, then write queries a site coordinator can actually act on — specific, neutral in tone, and answerable without three follow-ups. Then work the queries that came back: accept, re-query or close.
Outcome: queries raised, answered and closed
Update the trackers and records the study runs on. In clinical data, undocumented work is unfinished work — if it is not written down, traceable and dated, an inspector will treat it as though it never happened.
Outcome: an audit trail that stands up
Run and evidence data quality checks: does this edit check fire when it should, does it stay quiet when it should, and can you prove you tested both. This is the work behind a clean database, and it is where careful people get noticed.
Outcome: checks executed and evidenced
Take an assigned issue and see it through: what happened, what the data says, who needs to be asked, what the fix is and what it changes downstream. Investigating properly instead of guessing is most of the job.
Outcome: an issue closed with a reason recorded
Check your own work before anyone else does, then submit the day's deliverables. Self-review is the habit that separates a trainee who needs watching from one who can be given work.
Outcome: deliverables submitted for review
Go through the review comments on what you submitted: what was wrong, why it matters, and how it should have been done. Mistakes made here cost nothing. The same mistake on a live study costs a re-work, a deviation, or worse.
Outcome: corrections understood, not just marked
A representative training-day experience. Timings, task mix and workflows vary between organisations, studies and roles — this is how we structure practice, not a claim about any specific employer's working day.
Project-based training
Work on projects. Not just worksheets.
You work one study through the clinical data lifecycle, in sequence. Each activity feeds the next — so you learn why a decision taken at CRF design shows up as a problem four months later at lock, which is exactly the connection isolated topic-teaching never makes.
01
Study setup
Protocol read, schedule of assessments, data management plan.
02
CRF / eCRF design
Forms built from the protocol, CDASH-aligned and annotated.
03
Data entry
Entry and source verification workflow inside an EDC environment.
04
Data validation
The validation plan written, then tested against real entries.
05
Edit checks
Univariate and cross-form checks specified, programmed, UAT'd.
06
Query management
Discrepancies raised to site, tracked, chased and closed.
07
Data cleaning
The long middle of a study — ageing queries and stubborn data.
08
Medical coding
MedDRA for events, WHO Drug for medications, with query loops.
09
Reconciliation
Safety and vendor data matched against the clinical database.
10
Data review
Listings reviewed with medical and statistical eyes on the study.
11
Database freeze
Entry closed, outstanding items chased, readiness confirmed.
12
Database lock
Sign-offs, lock, archival — the deliverable the trial waits on.
Practice runs in training databases and demo environments built for this purpose. No live trial data is ever used.
Daily task-based learning
Train like you are already part of the team
Instead of theory assignments, you are given structured work. Every task carries the same four things a real assignment carries: an objective, a deliverable, a reviewer and a deadline.
Your training looks more like work
This is the shape of a day on the programme. It is a training environment built to feel like a work queue — not a pharmaceutical company's live system, and not connected to any real study or sponsor.
Once the platform side of Clini Careers is live, this is the view students will log into. Today the same structure runs through your trainer, your task sheet and your project file — the discipline is identical.
- Role
- CDM Trainee
- Completed
- 3
- Under review
- 1
- Needs action
- 2
Today's tasks
Due 17:00
- Review 25 subject records Done
- Identify and log data discrepancies Done
- Resolve assigned queries Done
- Complete validation activity DVP-014 In review
- Review coding discrepancies Open
- Submit daily deliverable Open
Illustrative training interface. Clini Careers is a training academy — this is not a sponsor or CRO system, and no live trial data is used at any point.
Task 01
Assigned
Subject data review
- Objective
- Review assigned subject records across visits and flag anything that cannot be right.
- Deliverable
- A discrepancy log with subject, form, field and the reason you flagged it.
- Review
- Checked for false positives as well as misses — over-flagging is its own problem.
Task 02
Assigned
Query writing and closure
- Objective
- Turn flagged discrepancies into queries a site can answer without a phone call.
- Deliverable
- Query text, priority and tracker entry; responses actioned and closed.
- Review
- Tone, specificity and whether the closure is properly evidenced.
Task 03
Assigned
Edit check specification and UAT
- Objective
- Specify a cross-form check, then test that it fires correctly and only when it should.
- Deliverable
- Check specification plus a UAT record with test data, expected and actual results.
- Review
- Whether the negative cases were tested, not only the ones that pass.
Task 04
Assigned
Medical coding pass
- Objective
- Code a batch of reported terms and raise coding queries where the term is unusable.
- Deliverable
- Coded listing with your decisions, plus the queries raised on ambiguous terms.
- Review
- Consistency across similar terms and whether you escalated the right ones.
Task 05
Assigned
External data reconciliation
- Objective
- Match a vendor or safety listing against the clinical database and resolve mismatches.
- Deliverable
- Reconciliation record: matched, unmatched, actioned, with the reason for each.
- Review
- Whether the unmatched records were investigated or quietly dropped.
Task 06
Assigned
Lock readiness check
- Objective
- Assess whether the study is genuinely ready to freeze, and evidence what is outstanding.
- Deliverable
- A readiness summary with open items, owners and what still blocks lock.
- Review
- Judgement — did you call it ready when it was not, and can you defend the call?
Role-based simulation
Learn what it feels like to own a role
You are not only a student attending sessions. Inside the project environment you hold a simulated role, with responsibilities attached to it — and the work is assigned to you by name.
Simulated role
CDM Trainee
The role you hold from module 05 onwards, on study ABC-CLINICAL-001.
- Complete the tasks assigned to you
- Review data and identify discrepancies
- Raise, track and close queries
- Follow SOP-style instructions exactly
- Submit deliverables in the required format
- Meet the deadline, or flag early that you will not
- Respond to review comments and rework
Responsibility grows as you do
Task complexity increases across the programme. You start with defined, bounded activities and finish with scenarios where you have to decide what needs doing — which is the difference between someone who can be given instructions and someone who can be given a study.
Stage 01
CDM Trainee
Defined tasks, close supervision, every deliverable reviewed.
Stage 02
Associate-level scenarios
Larger batches, competing deadlines, less hand-holding.
Stage 03
Senior task scenarios
Judgement calls, escalation decisions, reviewing others' work.
Stage 04
Project responsibility
Own a study area end to end and defend it at lock.
These are role-based simulations within a training programme. They are not employment, and they do not confer an employment designation or professional experience with an employer.
The environment
Experience the environment, not just the subject
Most freshers who struggle in their first six months do not struggle with clinical data. They struggle with how work works. These are the habits the programme is deliberately built to install.
Task ownership
Know precisely what has been assigned to you, what the deliverable is, and that nobody else is going to quietly finish it for you.
Timelines
Finish within the time given — and when you cannot, say so early instead of on the deadline. Studies are planned around both.
Quality
Understand why accuracy and documentation matter here specifically: a data point that cannot be traced is a data point a regulator can reject.
Communication
Raise an issue, give a status update and write a query in language that is precise, professional and free of blame.
Review
Have your work checked and corrected regularly enough that it stops being personal and starts being useful.
Collaboration & accountability
See how your piece connects to data entry, safety, coding and biostatistics — and what breaks downstream when you are late.
Review cycle
Make your mistakes here — before you make them at work
A large part of becoming job-ready is learning to respond to feedback, find your own errors and improve the work. Our environment gives you that loop, repeatedly, where the only cost of getting something wrong is doing it again properly.
Task
Submit
Review
Feedback
Correct
Resubmit
Approved
Work that does not pass review goes round again. Most do, at least once — that is the point of the loop, not a failure of it.
The change we are aiming for
From student to professional
We cannot promise you a job, and we will not. What we can describe honestly is the change the programme is designed to produce in how you work.
Before
Where most freshers start
- Knows the theory and the terminology
- Little or no practical exposure
- Unfamiliar with daily workflows
- Unsure what is expected in a workplace
- Nervous about scenario questions in interviews
During
What you do on the programme
- Performs assigned practical tasks daily
- Works realistic study scenarios end to end
- Produces deliverables to a required format
- Has work reviewed and corrects it
- Works to deadlines inside a project structure
- Builds professional working habits
After
Better prepared for the workplace
- Understands what a team will expect on day one
- Can discuss practical scenarios in an interview
- Can explain CDM workflows and why they exist
- Has a documented project to point at
- Adapts to a new role with less friction
Better prepared — not guaranteed placed. Hiring outcomes depend on your background, your effort, the interview and the market at the time. Anyone promising you a guaranteed job is telling you something they cannot control.
Situational practice
Learn through situations, not just definitions
Each case is worked the way a real one is: understand the situation, investigate it, decide the action, resolve it, and document what you did and why.
Case 01
Missing clinical data
A required field is empty across several visits for one site. Is it a site behaviour, a form design problem, or a protocol that does not match reality?
SituationInvestigateActionResolveDocument
Case 02
Inconsistent subject information
Demographics on one form contradict another. Both were entered by the same coordinator, both look plausible, and only one can be right.
SituationInvestigateActionResolveDocument
Case 03
Data validation failure
A check that passed UAT is firing on valid data in production. You have to decide whether to fix the check, the data, or the specification.
SituationInvestigateActionResolveDocument
Case 04
Query raised during data review
The site answers your query without changing anything and asks you to close it. It is not resolved. Now you have to push back, professionally.
SituationInvestigateActionResolveDocument
Case 05
Coding discrepancy
Two near-identical reported terms have been coded to different preferred terms. One of them is wrong, and the safety listing has already gone out.
SituationInvestigateActionResolveDocument
Case 06
External data reconciliation issue
The safety database has an event the clinical database does not. Somebody has to establish which system is wrong before lock, and it is you.
SituationInvestigateActionResolveDocument
The curriculum behind the work
Taught in the order the work actually happens
The practical environment sits on top of a proper syllabus. Twelve modules that follow one study from protocol to lock — not a list of topics in whatever sequence is easiest to teach. Each carries the CDISC variables and documents you will be asked about in an interview.
02
Regulations & data integrity
21 CFR 11Electronic records and signatures, ALCOA+ principles, audit trails, SOPs, the Data Management Plan, and what an inspector actually looks for.
03
CRF & eCRF design
CDASHTurning a protocol's schedule of assessments into forms, CDASH-aligned field naming, unique CRF annotation, and writing CRF completion guidelines that sites will follow.
04
Study database build
DB SPECDatabase specification documents, form and visit configuration, dictionaries and code lists, user roles, and the checks that must pass before go-live.
05
EDC systems in practice
SDVHands-on work in an electronic data capture environment: data entry, source data verification workflow, form freezing, protocol deviations and system reports.
06
Data validation & edit checks
DVPWriting the Data Validation Plan, designing univariate and cross-form checks, programming and testing them, and running structured User Acceptance Testing.
Tools & standards
The vocabulary an interviewer will test you on
You will have used each of these on the study you worked, which means you can talk about them from having done the task rather than from having read the definition.
EDC Data capture workflow
CRF eCRF design & annotation
DVP Edit check programming
UAT User acceptance testing
DM Query & discrepancy management
AE MedDRA coding
CM WHO Drug Dictionary
SAE Safety reconciliation
LB Central lab & vendor data
CDISC CDASH · SDTM · ADaM
REG 21 CFR Part 11 · ALCOA+
LOCK Database lock & archival
Add-on SAS & Excel for clinical data
Why this field
One of the few clinical research roles that genuinely hires freshers
Every clinical trial runs on data, and regulators require that data to be clean, traceable and auditable. That work is done by data management teams at CROs and sponsors — and it is a function that recruits life-science graduates and trains them up.
Open to your degree
B.Pharm, M.Pharm, Pharm.D, B.Sc and M.Sc in life sciences, biotechnology, microbiology and biochemistry, Nursing, BDS, BAMS, BHMS, BPT and MBBS graduates are all eligible. No coding background is assumed.
Where the jobs are
CROs and pharmaceutical sponsors hire data coordinators, data associates, medical coders, database programmers and data analysts across Hyderabad, Bengaluru, Pune, Mumbai, Chennai and remote teams.
A ladder that goes somewhere
Data Coordinator to Data Associate to Senior CDA to Lead Data Manager. The same skills also move sideways into pharmacovigilance, clinical programming and regulatory affairs.
Interview preparation
Go beyond interview theory
Interviews for data roles move to scenarios quickly. "What is clinical data management" is the warm-up question — the ones that decide the outcome start with "what would you do when…". Having done the work is what makes those answerable.
Scenario question
"A discrepancy has been identified during data review. Walk me through what you would do."
Studied the theory
Can define a discrepancy and name the query process. The answer runs about two sentences and then stops, because there is nothing behind it.
Worked the task
Checks the other forms first, decides whether it is genuine or a check firing wrongly, writes the query so the site can act, sets priority, tracks it, and says what evidence they would keep on closure.
Scenario question
"You receive a query that needs investigation before you can respond. How would you approach it?"
Studied the theory
Says they would "check the data and reply" — which is true, and tells the interviewer nothing about how they think.
Worked the task
Reconstructs what the check was testing, looks at the audit trail, works out whether other subjects are affected, escalates if the pattern is systemic, and documents the reasoning either way.
Practical experience helps you build structured answers. It does not guarantee an interview result — that depends on the role, the panel and you on the day.
The journey
Join, learn, work, get reviewed, get ready
The full path from your first phone call to sitting in interviews — with the practical environment sitting in the middle of it, where a normal course would have nothing but more classes.
01Join
Counselling call, honest fit check, batch chosen.
02Learn
Concepts, regulations and standards, taught in sequence.
03Enter the environment
You are given a study, a role and a queue.
04Daily tasks
Assigned work with objectives and deadlines.
05Project scenarios
Setup through to lock, connected end to end.
06Get reviewed
Written feedback on every deliverable.
07Improve
Rework, resubmit, and stop repeating the error.
08Build confidence
The work stops being unfamiliar.
09Interview prep
Scenario practice, CV built on your project.
10Explore opportunities
Referrals, our job board, and applying properly.
After the programme
Trained for the work, then pointed at the opportunities
We do not want students to simply finish a course. We want them to understand the kind of work they will meet in the industry — and then to go after it with a CV that reflects what they have actually done.
01
Learn the concept
The standard, the regulation, the document.
02
Experience the workflow
Where it sits in the study and what it touches.
03
Practise the task
Perform it, deliver it, get it reviewed.
04
Understand the responsibility
Know what breaks downstream if you get it wrong.
05
Prepare for the interview
Answer scenario questions from experience.
Explore relevant opportunities
Referrals to our hiring network and a live job board.
Who reviews your work
Sessions and reviews are run by trainers with hands-on clinical data management experience, not by presenters reading a deck. Review comments come from people who have had their own work reviewed on live studies.
Placement support, itemised
CV rebuilt around your project, LinkedIn tuned to recruiter keywords, three mock interviews with written feedback, and your profile shared with our hiring network. We do not promise a job — we keep working with you until you have one.
Live job board
Clinical data openings we are hearing about, open to our students and alumni — updated as roles come in.
Straight answers
What people ask about the practical environment
Is this real work experience I can put on my CV as employment?
No, and you should not present it that way. This is a training programme with a role-based simulation inside it. What goes on your CV is the training, the project you completed and the specific activities you performed — which is substantial, verifiable and exactly what a hiring manager wants to see from a fresher. Claiming employment you did not have is the one thing that will end an interview early.
Do you use real patient or trial data?
Never. All practice runs on training databases, demo environments and constructed study data built for teaching. Working with real subject data outside a sponsor's controlled environment would be a serious regulatory and privacy breach.
How is this different from a course with "practical sessions"?
A practical session is a demonstration you follow along with. Here the work is assigned to you with a deadline, you produce a deliverable in a required format, someone reviews it against criteria, and you rework it until it passes. The difference is ownership and review — not whether a screen was shared.
I am from a non-clinical background. Will I be able to keep up?
The concepts come first and the environment comes after, so you are never asked to perform a task you have not been taught. Tell us your background on the counselling call and we will be honest about the fit — including if we think a different path suits you better.
Does completing the programme guarantee a job?
No. Nobody can honestly promise that. What we do is prepare you properly, support you through applications and interviews, refer you into our hiring network, and keep working with you after the course ends. The outcome still depends on your background, your effort and the market.
Take it away with you
The full 12-module syllabus, as a PDF
Every module, the practical tasks attached to it, the add-on and the roles it prepares you for — the same document we send after a counselling call. Useful if you are comparing programmes.
Next step
Fifteen minutes will tell you whether this is right for you
No sales script. We will look at your degree, your year of passing and what you want, walk you through how the practical environment actually runs, and give you a straight answer — including when the answer is that you should look elsewhere.